Overview

Brought to you by YData

Dataset statistics

Number of variables33
Number of observations20718
Missing cells33
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.5 MiB
Average record size in memory2.1 KiB

Variable types

Numeric19
Text8
URL1
Categorical5

Alerts

Duration_ms is highly skewed (γ1 = 23.37595857) Skewed
Comments is highly skewed (γ1 = 44.28451689) Skewed
youtube_spotify_ratio is highly skewed (γ1 = 128.0401055) Skewed
Index is uniformly distributed Uniform
Index has unique values Unique
Key has 2305 (11.1%) zeros Zeros
Instrumentalness has 13529 (65.3%) zeros Zeros
Views has 471 (2.3%) zeros Zeros
Likes has 559 (2.7%) zeros Zeros
Comments has 1068 (5.2%) zeros Zeros
youtube_spotify_ratio has 460 (2.2%) zeros Zeros

Reproduction

Analysis started2025-04-18 04:29:57.142085
Analysis finished2025-04-18 04:30:55.422711
Duration58.28 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Index
Real number (ℝ)

Uniform  Unique 

Distinct20718
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10358.5
Minimum0
Maximum20717
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size162.0 KiB
2025-04-18T07:30:55.623953image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1035.85
Q15179.25
median10358.5
Q315537.75
95-th percentile19681.15
Maximum20717
Range20717
Interquartile range (IQR)10358.5

Descriptive statistics

Standard deviation5980.9158
Coefficient of variation (CV)0.57739207
Kurtosis-1.2
Mean10358.5
Median Absolute Deviation (MAD)5179.5
Skewness0
Sum2.146074 × 108
Variance35771354
MonotonicityStrictly increasing
2025-04-18T07:30:55.873174image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
13838 1
 
< 0.1%
13816 1
 
< 0.1%
13815 1
 
< 0.1%
13814 1
 
< 0.1%
13813 1
 
< 0.1%
13812 1
 
< 0.1%
13811 1
 
< 0.1%
13810 1
 
< 0.1%
13809 1
 
< 0.1%
Other values (20708) 20708
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
20717 1
< 0.1%
20716 1
< 0.1%
20715 1
< 0.1%
20714 1
< 0.1%
20713 1
< 0.1%
20712 1
< 0.1%
20711 1
< 0.1%
20710 1
< 0.1%
20709 1
< 0.1%
20708 1
< 0.1%

Artist
Text

Distinct2079
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2025-04-18T07:30:56.338866image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length45
Median length32
Mean length10.975384
Min length2

Characters and Unicode

Total characters227388
Distinct characters78
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowGorillaz
2nd rowGorillaz
3rd rowGorillaz
4th rowGorillaz
5th rowGorillaz
ValueCountFrequency (%)
the 840
 
2.1%
439
 
1.1%
los 290
 
0.7%
de 200
 
0.5%
la 150
 
0.4%
el 130
 
0.3%
of 130
 
0.3%
james 130
 
0.3%
john 120
 
0.3%
lil 119
 
0.3%
Other values (2944) 36662
93.5%
2025-04-18T07:30:57.120141image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 20743
 
9.1%
e 19633
 
8.6%
18492
 
8.1%
i 13968
 
6.1%
o 13563
 
6.0%
n 13036
 
5.7%
r 12386
 
5.4%
l 9566
 
4.2%
s 9026
 
4.0%
t 7418
 
3.3%
Other values (68) 89557
39.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 227388
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 20743
 
9.1%
e 19633
 
8.6%
18492
 
8.1%
i 13968
 
6.1%
o 13563
 
6.0%
n 13036
 
5.7%
r 12386
 
5.4%
l 9566
 
4.2%
s 9026
 
4.0%
t 7418
 
3.3%
Other values (68) 89557
39.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 227388
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 20743
 
9.1%
e 19633
 
8.6%
18492
 
8.1%
i 13968
 
6.1%
o 13563
 
6.0%
n 13036
 
5.7%
r 12386
 
5.4%
l 9566
 
4.2%
s 9026
 
4.0%
t 7418
 
3.3%
Other values (68) 89557
39.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 227388
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 20743
 
9.1%
e 19633
 
8.6%
18492
 
8.1%
i 13968
 
6.1%
o 13563
 
6.0%
n 13036
 
5.7%
r 12386
 
5.4%
l 9566
 
4.2%
s 9026
 
4.0%
t 7418
 
3.3%
Other values (68) 89557
39.4%
Distinct2079
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
https://open.spotify.com/artist/3AA28KZvwAUcZuOKwyblJQ
 
10
https://open.spotify.com/artist/2Jc4AEeBTE47KwuKgYOtcL
 
10
https://open.spotify.com/artist/0CEFCo8288kQU7mJi25s6E
 
10
https://open.spotify.com/artist/67hb7towEyKvt5Z8Bx306c
 
10
https://open.spotify.com/artist/4ZfEELHfyKd4odAb6YfDFw
 
10
Other values (2074)
20668 
ValueCountFrequency (%)
https://open.spotify.com/artist/3AA28KZvwAUcZuOKwyblJQ 10
 
< 0.1%
https://open.spotify.com/artist/2Jc4AEeBTE47KwuKgYOtcL 10
 
< 0.1%
https://open.spotify.com/artist/0CEFCo8288kQU7mJi25s6E 10
 
< 0.1%
https://open.spotify.com/artist/67hb7towEyKvt5Z8Bx306c 10
 
< 0.1%
https://open.spotify.com/artist/4ZfEELHfyKd4odAb6YfDFw 10
 
< 0.1%
https://open.spotify.com/artist/3HqSLMAZ3g3d5poNaI7GOU 10
 
< 0.1%
https://open.spotify.com/artist/2NMYOlZHIEsSq7pp5jBjic 10
 
< 0.1%
https://open.spotify.com/artist/6oMRQ5H3A2XA5I3RG3leni 10
 
< 0.1%
https://open.spotify.com/artist/5Pb27ujIyYb33zBqVysBkj 10
 
< 0.1%
https://open.spotify.com/artist/2Waw2sSbqvAwK8NwACNjVo 10
 
< 0.1%
Other values (2069) 20618
99.5%
ValueCountFrequency (%)
https 20718
100.0%
ValueCountFrequency (%)
open.spotify.com 20718
100.0%
ValueCountFrequency (%)
/artist/3AA28KZvwAUcZuOKwyblJQ 10
 
< 0.1%
/artist/2Jc4AEeBTE47KwuKgYOtcL 10
 
< 0.1%
/artist/0CEFCo8288kQU7mJi25s6E 10
 
< 0.1%
/artist/67hb7towEyKvt5Z8Bx306c 10
 
< 0.1%
/artist/4ZfEELHfyKd4odAb6YfDFw 10
 
< 0.1%
/artist/3HqSLMAZ3g3d5poNaI7GOU 10
 
< 0.1%
/artist/2NMYOlZHIEsSq7pp5jBjic 10
 
< 0.1%
/artist/6oMRQ5H3A2XA5I3RG3leni 10
 
< 0.1%
/artist/5Pb27ujIyYb33zBqVysBkj 10
 
< 0.1%
/artist/2Waw2sSbqvAwK8NwACNjVo 10
 
< 0.1%
Other values (2069) 20618
99.5%
ValueCountFrequency (%)
20718
100.0%
ValueCountFrequency (%)
20718
100.0%

Track
Text

Distinct17819
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-04-18T07:30:57.537414image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length195
Median length111
Mean length19.489043
Min length1

Characters and Unicode

Total characters403774
Distinct characters90
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15680 ?
Unique (%)75.7%

Sample

1st rowFeel Good Inc.
2nd rowRhinestone Eyes
3rd rowNew Gold (feat. Tame Impala and Bootie Brown)
4th rowOn Melancholy Hill
5th rowClint Eastwood
ValueCountFrequency (%)
3729
 
4.9%
feat 1744
 
2.3%
the 1632
 
2.1%
you 912
 
1.2%
me 903
 
1.2%
a 779
 
1.0%
i 705
 
0.9%
love 646
 
0.8%
of 590
 
0.8%
remix 587
 
0.8%
Other values (14798) 64627
84.1%
2025-04-18T07:30:58.474163image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
56212
 
13.9%
e 35103
 
8.7%
a 28285
 
7.0%
o 24115
 
6.0%
i 20485
 
5.1%
n 17879
 
4.4%
r 16936
 
4.2%
t 16151
 
4.0%
s 12420
 
3.1%
l 12098
 
3.0%
Other values (80) 164090
40.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 403774
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
56212
 
13.9%
e 35103
 
8.7%
a 28285
 
7.0%
o 24115
 
6.0%
i 20485
 
5.1%
n 17879
 
4.4%
r 16936
 
4.2%
t 16151
 
4.0%
s 12420
 
3.1%
l 12098
 
3.0%
Other values (80) 164090
40.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 403774
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
56212
 
13.9%
e 35103
 
8.7%
a 28285
 
7.0%
o 24115
 
6.0%
i 20485
 
5.1%
n 17879
 
4.4%
r 16936
 
4.2%
t 16151
 
4.0%
s 12420
 
3.1%
l 12098
 
3.0%
Other values (80) 164090
40.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 403774
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
56212
 
13.9%
e 35103
 
8.7%
a 28285
 
7.0%
o 24115
 
6.0%
i 20485
 
5.1%
n 17879
 
4.4%
r 16936
 
4.2%
t 16151
 
4.0%
s 12420
 
3.1%
l 12098
 
3.0%
Other values (80) 164090
40.6%

Album
Text

Distinct11929
Distinct (%)57.6%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-04-18T07:30:59.022962image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length195
Median length103
Mean length20.403176
Min length1

Characters and Unicode

Total characters422713
Distinct characters94
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7512 ?
Unique (%)36.3%

Sample

1st rowDemon Days
2nd rowPlastic Beach
3rd rowNew Gold (feat. Tame Impala and Bootie Brown)
4th rowPlastic Beach
5th rowGorillaz
ValueCountFrequency (%)
the 2698
 
3.7%
1574
 
2.2%
edition 851
 
1.2%
deluxe 823
 
1.1%
of 817
 
1.1%
original 732
 
1.0%
soundtrack 716
 
1.0%
motion 687
 
1.0%
picture 671
 
0.9%
a 611
 
0.8%
Other values (11756) 62107
85.9%
2025-04-18T07:30:59.719977image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
51624
 
12.2%
e 35554
 
8.4%
a 26597
 
6.3%
o 25212
 
6.0%
i 24287
 
5.7%
n 20857
 
4.9%
r 19266
 
4.6%
t 17500
 
4.1%
s 14397
 
3.4%
l 13812
 
3.3%
Other values (84) 173607
41.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 422713
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
51624
 
12.2%
e 35554
 
8.4%
a 26597
 
6.3%
o 25212
 
6.0%
i 24287
 
5.7%
n 20857
 
4.9%
r 19266
 
4.6%
t 17500
 
4.1%
s 14397
 
3.4%
l 13812
 
3.3%
Other values (84) 173607
41.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 422713
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
51624
 
12.2%
e 35554
 
8.4%
a 26597
 
6.3%
o 25212
 
6.0%
i 24287
 
5.7%
n 20857
 
4.9%
r 19266
 
4.6%
t 17500
 
4.1%
s 14397
 
3.4%
l 13812
 
3.3%
Other values (84) 173607
41.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 422713
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
51624
 
12.2%
e 35554
 
8.4%
a 26597
 
6.3%
o 25212
 
6.0%
i 24287
 
5.7%
n 20857
 
4.9%
r 19266
 
4.6%
t 17500
 
4.1%
s 14397
 
3.4%
l 13812
 
3.3%
Other values (84) 173607
41.1%

Album_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
album
14926 
single
5004 
compilation
 
788

Length

Max length11
Median length5
Mean length5.4697365
Min length5

Characters and Unicode

Total characters113322
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowalbum
2nd rowalbum
3rd rowsingle
4th rowalbum
5th rowalbum

Common Values

ValueCountFrequency (%)
album 14926
72.0%
single 5004
 
24.2%
compilation 788
 
3.8%

Length

2025-04-18T07:30:59.926110image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-18T07:31:00.089663image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
album 14926
72.0%
single 5004
 
24.2%
compilation 788
 
3.8%

Most occurring characters

ValueCountFrequency (%)
l 20718
18.3%
a 15714
13.9%
m 15714
13.9%
b 14926
13.2%
u 14926
13.2%
i 6580
 
5.8%
n 5792
 
5.1%
s 5004
 
4.4%
g 5004
 
4.4%
e 5004
 
4.4%
Other values (4) 3940
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113322
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 20718
18.3%
a 15714
13.9%
m 15714
13.9%
b 14926
13.2%
u 14926
13.2%
i 6580
 
5.8%
n 5792
 
5.1%
s 5004
 
4.4%
g 5004
 
4.4%
e 5004
 
4.4%
Other values (4) 3940
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113322
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 20718
18.3%
a 15714
13.9%
m 15714
13.9%
b 14926
13.2%
u 14926
13.2%
i 6580
 
5.8%
n 5792
 
5.1%
s 5004
 
4.4%
g 5004
 
4.4%
e 5004
 
4.4%
Other values (4) 3940
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113322
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 20718
18.3%
a 15714
13.9%
m 15714
13.9%
b 14926
13.2%
u 14926
13.2%
i 6580
 
5.8%
n 5792
 
5.1%
s 5004
 
4.4%
g 5004
 
4.4%
e 5004
 
4.4%
Other values (4) 3940
 
3.5%

Uri
Text

Distinct18862
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2025-04-18T07:31:00.370444image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters745848
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17408 ?
Unique (%)84.0%

Sample

1st rowspotify:track:0d28khcov6AiegSCpG5TuT
2nd rowspotify:track:1foMv2HQwfQ2vntFf9HFeG
3rd rowspotify:track:64dLd6rVqDLtkXFYrEUHIU
4th rowspotify:track:0q6LuUqGLUiCPP1cbdwFs3
5th rowspotify:track:7yMiX7n9SBvadzox8T5jzT
ValueCountFrequency (%)
spotify:track:0tzixmhnqfe6s6sirstoxw 24
 
0.1%
spotify:track:3esjmgwqobrx5wbfctvptz 19
 
0.1%
spotify:track:0xfqb7pnft90a7frxjmwgz 9
 
< 0.1%
spotify:track:5iis9j2sptruy0vipfvig1 9
 
< 0.1%
spotify:track:114xlnybqwtikodru8lajn 9
 
< 0.1%
spotify:track:1mhhomaxgrpmbroxkpwssa 9
 
< 0.1%
spotify:track:1fsydjalyhaabwhiggyfdq 9
 
< 0.1%
spotify:track:3fifrouyur8j5mtm78wuxq 7
 
< 0.1%
spotify:track:7qoihutxu269zqo9pg5ioj 7
 
< 0.1%
spotify:track:0ctzpaohtzvlv3ffcsvpqo 6
 
< 0.1%
Other values (18852) 20610
99.5%
2025-04-18T07:31:00.865594image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 48505
 
6.5%
: 41436
 
5.6%
r 27966
 
3.7%
o 27879
 
3.7%
i 27843
 
3.7%
y 27807
 
3.7%
p 27779
 
3.7%
s 27731
 
3.7%
f 27727
 
3.7%
k 27707
 
3.7%
Other values (53) 433468
58.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 745848
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 48505
 
6.5%
: 41436
 
5.6%
r 27966
 
3.7%
o 27879
 
3.7%
i 27843
 
3.7%
y 27807
 
3.7%
p 27779
 
3.7%
s 27731
 
3.7%
f 27727
 
3.7%
k 27707
 
3.7%
Other values (53) 433468
58.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 745848
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 48505
 
6.5%
: 41436
 
5.6%
r 27966
 
3.7%
o 27879
 
3.7%
i 27843
 
3.7%
y 27807
 
3.7%
p 27779
 
3.7%
s 27731
 
3.7%
f 27727
 
3.7%
k 27707
 
3.7%
Other values (53) 433468
58.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 745848
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 48505
 
6.5%
: 41436
 
5.6%
r 27966
 
3.7%
o 27879
 
3.7%
i 27843
 
3.7%
y 27807
 
3.7%
p 27779
 
3.7%
s 27731
 
3.7%
f 27727
 
3.7%
k 27707
 
3.7%
Other values (53) 433468
58.1%

Danceability
Real number (ℝ)

Distinct898
Distinct (%)4.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.61977745
Minimum0
Maximum0.975
Zeros17
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size162.0 KiB
2025-04-18T07:31:01.081703image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.319
Q10.518
median0.637
Q30.74025
95-th percentile0.861
Maximum0.975
Range0.975
Interquartile range (IQR)0.22225

Descriptive statistics

Standard deviation0.16527239
Coefficient of variation (CV)0.26666409
Kurtosis0.13707564
Mean0.61977745
Median Absolute Deviation (MAD)0.11
Skewness-0.55015414
Sum12839.31
Variance0.027314963
MonotonicityNot monotonic
2025-04-18T07:31:01.326831image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.687 78
 
0.4%
0.671 74
 
0.4%
0.626 69
 
0.3%
0.647 68
 
0.3%
0.682 67
 
0.3%
0.585 66
 
0.3%
0.673 64
 
0.3%
0.681 64
 
0.3%
0.638 63
 
0.3%
0.646 62
 
0.3%
Other values (888) 20041
96.7%
ValueCountFrequency (%)
0 17
0.1%
0.0532 1
 
< 0.1%
0.0619 1
 
< 0.1%
0.0623 1
 
< 0.1%
0.064 1
 
< 0.1%
0.0649 1
 
< 0.1%
0.065 1
 
< 0.1%
0.0673 1
 
< 0.1%
0.0686 1
 
< 0.1%
0.069 1
 
< 0.1%
ValueCountFrequency (%)
0.975 3
< 0.1%
0.973 1
 
< 0.1%
0.971 2
< 0.1%
0.97 4
< 0.1%
0.969 1
 
< 0.1%
0.968 1
 
< 0.1%
0.967 3
< 0.1%
0.966 1
 
< 0.1%
0.965 2
< 0.1%
0.964 4
< 0.1%

Energy
Real number (ℝ)

Distinct1247
Distinct (%)6.0%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.63525034
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size162.0 KiB
2025-04-18T07:31:01.548580image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.22
Q10.507
median0.666
Q30.798
95-th percentile0.929
Maximum1
Range1
Interquartile range (IQR)0.291

Descriptive statistics

Standard deviation0.21414686
Coefficient of variation (CV)0.33710626
Kurtosis0.13887603
Mean0.63525034
Median Absolute Deviation (MAD)0.143
Skewness-0.71485282
Sum13159.846
Variance0.045858879
MonotonicityNot monotonic
2025-04-18T07:31:01.756242image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.572 60
 
0.3%
0.711 57
 
0.3%
0.72 57
 
0.3%
0.785 56
 
0.3%
0.768 56
 
0.3%
0.674 55
 
0.3%
0.703 54
 
0.3%
0.782 54
 
0.3%
0.723 53
 
0.3%
0.662 53
 
0.3%
Other values (1237) 20161
97.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.0001 2
< 0.1%
0.0003 2
< 0.1%
0.0012 3
< 0.1%
0.0014 1
 
< 0.1%
0.0017 1
 
< 0.1%
0.0019 2
< 0.1%
0.002 1
 
< 0.1%
0.0021 1
 
< 0.1%
0.0022 2
< 0.1%
ValueCountFrequency (%)
1 6
< 0.1%
0.999 1
 
< 0.1%
0.998 5
< 0.1%
0.997 3
 
< 0.1%
0.996 7
< 0.1%
0.995 10
< 0.1%
0.994 10
< 0.1%
0.993 5
< 0.1%
0.992 5
< 0.1%
0.991 8
< 0.1%

Key
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.3003476
Minimum0
Maximum11
Zeros2305
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size162.0 KiB
2025-04-18T07:31:01.920705image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5764485
Coefficient of variation (CV)0.67475736
Kurtosis-1.2980009
Mean5.3003476
Median Absolute Deviation (MAD)3
Skewness-0.0045108915
Sum109802
Variance12.790984
MonotonicityNot monotonic
2025-04-18T07:31:02.061171image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 2305
11.1%
7 2252
10.9%
1 2211
10.7%
2 2021
9.8%
9 1979
9.6%
5 1731
8.4%
11 1667
8.0%
4 1515
7.3%
8 1483
7.2%
6 1443
7.0%
Other values (2) 2109
10.2%
ValueCountFrequency (%)
0 2305
11.1%
1 2211
10.7%
2 2021
9.8%
3 670
 
3.2%
4 1515
7.3%
5 1731
8.4%
6 1443
7.0%
7 2252
10.9%
8 1483
7.2%
9 1979
9.6%
ValueCountFrequency (%)
11 1667
8.0%
10 1439
6.9%
9 1979
9.6%
8 1483
7.2%
7 2252
10.9%
6 1443
7.0%
5 1731
8.4%
4 1515
7.3%
3 670
 
3.2%
2 2021
9.8%

Loudness
Real number (ℝ)

Distinct9417
Distinct (%)45.5%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-7.6716803
Minimum-46.251
Maximum0.92
Zeros0
Zeros (%)0.0%
Negative20710
Negative (%)> 99.9%
Memory size162.0 KiB
2025-04-18T07:31:02.221000image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-46.251
5-th percentile-15.8895
Q1-8.858
median-6.536
Q3-4.931
95-th percentile-3.199
Maximum0.92
Range47.171
Interquartile range (IQR)3.927

Descriptive statistics

Standard deviation4.6327486
Coefficient of variation (CV)-0.60387664
Kurtosis10.735181
Mean-7.6716803
Median Absolute Deviation (MAD)1.835
Skewness-2.700817
Sum-158926.53
Variance21.462359
MonotonicityNot monotonic
2025-04-18T07:31:02.414359image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-7.818 25
 
0.1%
-7.768 21
 
0.1%
-4.501 16
 
0.1%
-6.887 15
 
0.1%
-6.253 14
 
0.1%
-6.246 12
 
0.1%
-5.76 12
 
0.1%
-5.077 12
 
0.1%
-5.549 11
 
0.1%
-4.592 11
 
0.1%
Other values (9407) 20567
99.3%
ValueCountFrequency (%)
-46.251 1
< 0.1%
-44.761 1
< 0.1%
-43.988 1
< 0.1%
-41.932 1
< 0.1%
-41.766 1
< 0.1%
-41.696 1
< 0.1%
-41.53 2
< 0.1%
-41.001 1
< 0.1%
-39.919 1
< 0.1%
-39.869 1
< 0.1%
ValueCountFrequency (%)
0.92 1
 
< 0.1%
0.829 1
 
< 0.1%
0.561 1
 
< 0.1%
0.522 1
 
< 0.1%
0.175 1
 
< 0.1%
0.006 1
 
< 0.1%
-0.007 1
 
< 0.1%
-0.14 1
 
< 0.1%
-0.142 1
 
< 0.1%
-0.155 4
< 0.1%

Speechiness
Real number (ℝ)

Distinct1303
Distinct (%)6.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.096456005
Minimum0
Maximum0.964
Zeros17
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size162.0 KiB
2025-04-18T07:31:02.587507image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0278
Q10.0357
median0.0505
Q30.103
95-th percentile0.324
Maximum0.964
Range0.964
Interquartile range (IQR)0.0673

Descriptive statistics

Standard deviation0.11196003
Coefficient of variation (CV)1.1607367
Kurtosis16.499958
Mean0.096456005
Median Absolute Deviation (MAD)0.0192
Skewness3.3736906
Sum1998.1826
Variance0.012535048
MonotonicityNot monotonic
2025-04-18T07:31:02.770639image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0324 72
 
0.3%
0.0305 68
 
0.3%
0.0288 65
 
0.3%
0.0317 65
 
0.3%
0.0293 64
 
0.3%
0.0326 64
 
0.3%
0.0315 63
 
0.3%
0.0377 63
 
0.3%
0.0308 62
 
0.3%
0.0306 62
 
0.3%
Other values (1293) 20068
96.9%
ValueCountFrequency (%)
0 17
0.1%
0.022 1
 
< 0.1%
0.0222 1
 
< 0.1%
0.0224 1
 
< 0.1%
0.0225 1
 
< 0.1%
0.0226 1
 
< 0.1%
0.0227 1
 
< 0.1%
0.0229 1
 
< 0.1%
0.023 2
 
< 0.1%
0.0231 1
 
< 0.1%
ValueCountFrequency (%)
0.964 1
< 0.1%
0.962 1
< 0.1%
0.961 2
< 0.1%
0.96 2
< 0.1%
0.959 2
< 0.1%
0.956 1
< 0.1%
0.955 1
< 0.1%
0.954 1
< 0.1%
0.953 1
< 0.1%
0.952 1
< 0.1%

Acousticness
Real number (ℝ)

Distinct1881
Distinct (%)9.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.29153519
Minimum0
Maximum0.996
Zeros134
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size162.0 KiB
2025-04-18T07:31:02.954153image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0016
Q10.0452
median0.193
Q30.47725
95-th percentile0.885
Maximum0.996
Range0.996
Interquartile range (IQR)0.43205

Descriptive statistics

Standard deviation0.28629898
Coefficient of variation (CV)0.98203918
Kurtosis-0.38256978
Mean0.29153519
Median Absolute Deviation (MAD)0.174
Skewness0.88308992
Sum6039.443
Variance0.081967106
MonotonicityNot monotonic
2025-04-18T07:31:03.137060image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0001 145
 
0.7%
0 134
 
0.6%
0.0002 99
 
0.5%
0.0003 86
 
0.4%
0.0004 68
 
0.3%
0.0006 54
 
0.3%
0.0007 54
 
0.3%
0.0011 52
 
0.3%
0.114 50
 
0.2%
0.001 50
 
0.2%
Other values (1871) 19924
96.2%
ValueCountFrequency (%)
0 134
0.6%
0.0001 145
0.7%
0.0002 99
0.5%
0.0003 86
0.4%
0.0004 68
0.3%
0.0005 48
 
0.2%
0.0006 54
 
0.3%
0.0007 54
 
0.3%
0.0008 43
 
0.2%
0.0009 49
 
0.2%
ValueCountFrequency (%)
0.996 19
0.1%
0.995 27
0.1%
0.994 27
0.1%
0.993 21
0.1%
0.992 20
0.1%
0.991 18
0.1%
0.99 13
0.1%
0.989 17
0.1%
0.988 16
0.1%
0.987 16
0.1%

Instrumentalness
Real number (ℝ)

Zeros 

Distinct1354
Distinct (%)6.5%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.055959852
Minimum0
Maximum1
Zeros13529
Zeros (%)65.3%
Negative0
Negative (%)0.0%
Memory size162.0 KiB
2025-04-18T07:31:03.326452image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.0005
95-th percentile0.58
Maximum1
Range1
Interquartile range (IQR)0.0005

Descriptive statistics

Standard deviation0.19326253
Coefficient of variation (CV)3.4535925
Kurtosis12.664098
Mean0.055959852
Median Absolute Deviation (MAD)0
Skewness3.7200367
Sum1159.2643
Variance0.037350405
MonotonicityNot monotonic
2025-04-18T07:31:03.511331image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13529
65.3%
0.0001 1028
 
5.0%
0.0002 484
 
2.3%
0.0003 272
 
1.3%
0.0004 201
 
1.0%
0.0005 174
 
0.8%
0.0006 142
 
0.7%
0.0009 114
 
0.6%
0.0007 95
 
0.5%
0.0008 87
 
0.4%
Other values (1344) 4590
 
22.2%
ValueCountFrequency (%)
0 13529
65.3%
0.0001 1028
 
5.0%
0.0002 484
 
2.3%
0.0003 272
 
1.3%
0.0004 201
 
1.0%
0.0005 174
 
0.8%
0.0006 142
 
0.7%
0.0007 95
 
0.5%
0.0008 87
 
0.4%
0.0009 114
 
0.6%
ValueCountFrequency (%)
1 8
< 0.1%
0.999 1
 
< 0.1%
0.995 3
 
< 0.1%
0.993 1
 
< 0.1%
0.992 2
 
< 0.1%
0.989 2
 
< 0.1%
0.988 4
< 0.1%
0.986 2
 
< 0.1%
0.985 1
 
< 0.1%
0.983 1
 
< 0.1%

Liveness
Real number (ℝ)

Distinct1536
Distinct (%)7.4%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.193521
Minimum0.0145
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size162.0 KiB
2025-04-18T07:31:03.704930image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.0145
5-th percentile0.058375
Q10.0941
median0.125
Q30.237
95-th percentile0.573
Maximum1
Range0.9855
Interquartile range (IQR)0.1429

Descriptive statistics

Standard deviation0.1685309
Coefficient of variation (CV)0.87086623
Kurtosis5.8521745
Mean0.193521
Median Absolute Deviation (MAD)0.0453
Skewness2.310133
Sum4008.981
Variance0.028402665
MonotonicityNot monotonic
2025-04-18T07:31:03.904543image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.11 237
 
1.1%
0.109 226
 
1.1%
0.111 215
 
1.0%
0.107 215
 
1.0%
0.108 211
 
1.0%
0.104 201
 
1.0%
0.103 195
 
0.9%
0.105 185
 
0.9%
0.101 185
 
0.9%
0.112 175
 
0.8%
Other values (1526) 18671
90.1%
ValueCountFrequency (%)
0.0145 1
< 0.1%
0.015 1
< 0.1%
0.0157 1
< 0.1%
0.0158 1
< 0.1%
0.0181 1
< 0.1%
0.0182 1
< 0.1%
0.0188 1
< 0.1%
0.019 2
< 0.1%
0.0199 1
< 0.1%
0.02 1
< 0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.997 1
 
< 0.1%
0.99 1
 
< 0.1%
0.986 1
 
< 0.1%
0.984 7
< 0.1%
0.983 5
< 0.1%
0.982 2
 
< 0.1%
0.98 2
 
< 0.1%
0.978 2
 
< 0.1%
0.977 5
< 0.1%

Valence
Real number (ℝ)

Distinct1292
Distinct (%)6.2%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.52985332
Minimum0
Maximum0.993
Zeros47
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size162.0 KiB
2025-04-18T07:31:04.101609image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.119
Q10.339
median0.537
Q30.72625
95-th percentile0.921
Maximum0.993
Range0.993
Interquartile range (IQR)0.38725

Descriptive statistics

Standard deviation0.24544083
Coefficient of variation (CV)0.4632241
Kurtosis-0.92958778
Mean0.52985332
Median Absolute Deviation (MAD)0.194
Skewness-0.10078586
Sum10976.441
Variance0.060241199
MonotonicityNot monotonic
2025-04-18T07:31:04.344651image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961 71
 
0.3%
0.785 51
 
0.2%
0.962 47
 
0.2%
0 47
 
0.2%
0.491 44
 
0.2%
0.595 44
 
0.2%
0.637 44
 
0.2%
0.964 43
 
0.2%
0.285 43
 
0.2%
0.284 42
 
0.2%
Other values (1282) 20240
97.7%
ValueCountFrequency (%)
0 47
0.2%
0.0024 1
 
< 0.1%
0.0029 1
 
< 0.1%
0.0099 2
 
< 0.1%
0.0129 1
 
< 0.1%
0.0144 1
 
< 0.1%
0.0153 1
 
< 0.1%
0.0154 2
 
< 0.1%
0.0185 3
 
< 0.1%
0.0256 1
 
< 0.1%
ValueCountFrequency (%)
0.993 1
 
< 0.1%
0.991 1
 
< 0.1%
0.99 1
 
< 0.1%
0.989 1
 
< 0.1%
0.986 1
 
< 0.1%
0.985 2
< 0.1%
0.984 2
< 0.1%
0.982 2
< 0.1%
0.981 2
< 0.1%
0.98 4
< 0.1%

Tempo
Real number (ℝ)

Distinct15024
Distinct (%)72.5%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean120.63834
Minimum0
Maximum243.372
Zeros17
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size162.0 KiB
2025-04-18T07:31:04.576125image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile78.4305
Q197.002
median119.965
Q3139.935
95-th percentile174.794
Maximum243.372
Range243.372
Interquartile range (IQR)42.933

Descriptive statistics

Standard deviation29.579018
Coefficient of variation (CV)0.24518754
Kurtosis-0.13017291
Mean120.63834
Median Absolute Deviation (MAD)21.2055
Skewness0.3931929
Sum2499143.9
Variance874.91828
MonotonicityNot monotonic
2025-04-18T07:31:04.807690image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.986 24
 
0.1%
106.002 19
 
0.1%
0 17
 
0.1%
120.031 12
 
0.1%
119.982 11
 
0.1%
129.971 10
 
< 0.1%
120.057 10
 
< 0.1%
100.015 10
 
< 0.1%
140.006 10
 
< 0.1%
106.001 9
 
< 0.1%
Other values (15014) 20584
99.4%
ValueCountFrequency (%)
0 17
0.1%
37.114 1
 
< 0.1%
38.137 1
 
< 0.1%
43.509 1
 
< 0.1%
45.397 1
 
< 0.1%
46.718 1
 
< 0.1%
47.362 3
 
< 0.1%
48.028 1
 
< 0.1%
48.19 2
 
< 0.1%
48.637 1
 
< 0.1%
ValueCountFrequency (%)
243.372 1
< 0.1%
236.059 1
< 0.1%
220.099 1
< 0.1%
215.918 1
< 0.1%
214.025 1
< 0.1%
213.503 1
< 0.1%
211.958 1
< 0.1%
210.857 1
< 0.1%
209.953 1
< 0.1%
209.795 1
< 0.1%

Duration_ms
Real number (ℝ)

Skewed 

Distinct14690
Distinct (%)70.9%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean224717.58
Minimum30985
Maximum4676058
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size162.0 KiB
2025-04-18T07:31:05.007190image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum30985
5-th percentile133707.75
Q1180009.5
median213284.5
Q3252443
95-th percentile335837
Maximum4676058
Range4645073
Interquartile range (IQR)72433.5

Descriptive statistics

Standard deviation124790.54
Coefficient of variation (CV)0.55532168
Kurtosis788.22417
Mean224717.58
Median Absolute Deviation (MAD)35751.5
Skewness23.375959
Sum4.6552494 × 109
Variance1.557268 × 1010
MonotonicityNot monotonic
2025-04-18T07:31:05.236963image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
237467 26
 
0.1%
217547 19
 
0.1%
216000 18
 
0.1%
180000 12
 
0.1%
240000 12
 
0.1%
160000 12
 
0.1%
170000 10
 
< 0.1%
31000 10
 
< 0.1%
192000 10
 
< 0.1%
252443 9
 
< 0.1%
Other values (14680) 20578
99.3%
ValueCountFrequency (%)
30985 1
 
< 0.1%
31000 10
< 0.1%
31437 1
 
< 0.1%
35000 1
 
< 0.1%
37000 1
 
< 0.1%
38000 2
 
< 0.1%
39000 2
 
< 0.1%
40000 1
 
< 0.1%
41000 1
 
< 0.1%
42000 1
 
< 0.1%
ValueCountFrequency (%)
4676058 1
 
< 0.1%
4581483 9
< 0.1%
4120258 1
 
< 0.1%
3340672 1
 
< 0.1%
1484260 1
 
< 0.1%
1427979 1
 
< 0.1%
1330157 2
 
< 0.1%
983432 1
 
< 0.1%
975267 1
 
< 0.1%
944325 1
 
< 0.1%
Distinct18155
Distinct (%)87.6%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2025-04-18T07:31:05.529341image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length43
Median length43
Mean length42.274061
Min length11

Characters and Unicode

Total characters875834
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16611 ?
Unique (%)80.2%

Sample

1st rowhttps://www.youtube.com/watch?v=HyHNuVaZJ-k
2nd rowhttps://www.youtube.com/watch?v=yYDmaexVHic
3rd rowhttps://www.youtube.com/watch?v=qJa-VFwPpYA
4th rowhttps://www.youtube.com/watch?v=04mfKJWDSzI
5th rowhttps://www.youtube.com/watch?v=1V_xRb0x9aw
ValueCountFrequency (%)
non-youtube 470
 
2.3%
https://www.youtube.com/watch?v=gqovxbflwjy 18
 
0.1%
https://www.youtube.com/watch?v=zxg1yj1si58 13
 
0.1%
https://www.youtube.com/watch?v=2znsgszhbfm 12
 
0.1%
https://www.youtube.com/watch?v=glfy8m3wjoe 10
 
< 0.1%
https://www.youtube.com/watch?v=6nb-prb-4p0 10
 
< 0.1%
https://www.youtube.com/watch?v=69-jna4qlsm 10
 
< 0.1%
https://www.youtube.com/watch?v=n-ak6jnyfmk 10
 
< 0.1%
https://www.youtube.com/watch?v=lw57rk7hr3y 10
 
< 0.1%
https://www.youtube.com/watch?v=epjtnspfwk8 10
 
< 0.1%
Other values (18145) 20145
97.2%
2025-04-18T07:31:06.397629image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
w 85465
 
9.8%
t 84729
 
9.7%
/ 60744
 
6.9%
o 45752
 
5.2%
c 44906
 
5.1%
u 44677
 
5.1%
h 43714
 
5.0%
. 40496
 
4.6%
s 24604
 
2.8%
e 23885
 
2.7%
Other values (59) 376862
43.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 875834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
w 85465
 
9.8%
t 84729
 
9.7%
/ 60744
 
6.9%
o 45752
 
5.2%
c 44906
 
5.1%
u 44677
 
5.1%
h 43714
 
5.0%
. 40496
 
4.6%
s 24604
 
2.8%
e 23885
 
2.7%
Other values (59) 376862
43.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 875834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
w 85465
 
9.8%
t 84729
 
9.7%
/ 60744
 
6.9%
o 45752
 
5.2%
c 44906
 
5.1%
u 44677
 
5.1%
h 43714
 
5.0%
. 40496
 
4.6%
s 24604
 
2.8%
e 23885
 
2.7%
Other values (59) 376862
43.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 875834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
w 85465
 
9.8%
t 84729
 
9.7%
/ 60744
 
6.9%
o 45752
 
5.2%
c 44906
 
5.1%
u 44677
 
5.1%
h 43714
 
5.0%
. 40496
 
4.6%
s 24604
 
2.8%
e 23885
 
2.7%
Other values (59) 376862
43.0%

Title
Text

Distinct18147
Distinct (%)87.6%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2025-04-18T07:31:06.768735image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length188
Median length92
Mean length49.287479
Min length3

Characters and Unicode

Total characters1021138
Distinct characters94
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16596 ?
Unique (%)80.1%

Sample

1st rowGorillaz - Feel Good Inc. (Official Video)
2nd rowGorillaz - Rhinestone Eyes [Storyboard Film] (Official Music Video)
3rd rowGorillaz - New Gold ft. Tame Impala & Bootie Brown (Official Visualiser)
4th rowGorillaz - On Melancholy Hill (Official Video)
5th rowGorillaz - Clint Eastwood (Official Video)
ValueCountFrequency (%)
23995
 
13.3%
video 9476
 
5.2%
official 7510
 
4.1%
music 3135
 
1.7%
the 2652
 
1.5%
ft 1899
 
1.0%
oficial 1409
 
0.8%
feat 1119
 
0.6%
you 942
 
0.5%
me 911
 
0.5%
Other values (20498) 127957
70.7%
2025-04-18T07:31:07.346339image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
161572
 
15.8%
i 72129
 
7.1%
e 68869
 
6.7%
a 65685
 
6.4%
o 52354
 
5.1%
n 37101
 
3.6%
l 34572
 
3.4%
r 34215
 
3.4%
t 27348
 
2.7%
s 27012
 
2.6%
Other values (84) 440281
43.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1021138
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
161572
 
15.8%
i 72129
 
7.1%
e 68869
 
6.7%
a 65685
 
6.4%
o 52354
 
5.1%
n 37101
 
3.6%
l 34572
 
3.4%
r 34215
 
3.4%
t 27348
 
2.7%
s 27012
 
2.6%
Other values (84) 440281
43.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1021138
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
161572
 
15.8%
i 72129
 
7.1%
e 68869
 
6.7%
a 65685
 
6.4%
o 52354
 
5.1%
n 37101
 
3.6%
l 34572
 
3.4%
r 34215
 
3.4%
t 27348
 
2.7%
s 27012
 
2.6%
Other values (84) 440281
43.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1021138
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
161572
 
15.8%
i 72129
 
7.1%
e 68869
 
6.7%
a 65685
 
6.4%
o 52354
 
5.1%
n 37101
 
3.6%
l 34572
 
3.4%
r 34215
 
3.4%
t 27348
 
2.7%
s 27012
 
2.6%
Other values (84) 440281
43.1%
Distinct6715
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2025-04-18T07:31:07.685978image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length60
Median length46
Mean length13.214837
Min length1

Characters and Unicode

Total characters273785
Distinct characters86
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3928 ?
Unique (%)19.0%

Sample

1st rowGorillaz
2nd rowGorillaz
3rd rowGorillaz
4th rowGorillaz
5th rowGorillaz
ValueCountFrequency (%)
1330
 
3.7%
topic 1068
 
3.0%
music 770
 
2.2%
non-youtube 470
 
1.3%
records 387
 
1.1%
the 319
 
0.9%
t-series 249
 
0.7%
official 182
 
0.5%
oficial 137
 
0.4%
tv 132
 
0.4%
Other values (7922) 30519
85.8%
2025-04-18T07:31:08.210939image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 19655
 
7.2%
a 18367
 
6.7%
i 17136
 
6.3%
o 15744
 
5.8%
14970
 
5.5%
V 14490
 
5.3%
n 13424
 
4.9%
r 11843
 
4.3%
s 10339
 
3.8%
l 9315
 
3.4%
Other values (76) 128502
46.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 273785
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 19655
 
7.2%
a 18367
 
6.7%
i 17136
 
6.3%
o 15744
 
5.8%
14970
 
5.5%
V 14490
 
5.3%
n 13424
 
4.9%
r 11843
 
4.3%
s 10339
 
3.8%
l 9315
 
3.4%
Other values (76) 128502
46.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 273785
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 19655
 
7.2%
a 18367
 
6.7%
i 17136
 
6.3%
o 15744
 
5.8%
14970
 
5.5%
V 14490
 
5.3%
n 13424
 
4.9%
r 11843
 
4.3%
s 10339
 
3.8%
l 9315
 
3.4%
Other values (76) 128502
46.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 273785
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 19655
 
7.2%
a 18367
 
6.7%
i 17136
 
6.3%
o 15744
 
5.8%
14970
 
5.5%
V 14490
 
5.3%
n 13424
 
4.9%
r 11843
 
4.3%
s 10339
 
3.8%
l 9315
 
3.4%
Other values (76) 128502
46.9%

Views
Real number (ℝ)

Zeros 

Distinct19245
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91806786
Minimum0
Maximum8.0796494 × 109
Zeros471
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size162.0 KiB
2025-04-18T07:31:08.402034image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15404.45
Q11484004
median13373900
Q367492280
95-th percentile4.2621334 × 108
Maximum8.0796494 × 109
Range8.0796494 × 109
Interquartile range (IQR)66008276

Descriptive statistics

Standard deviation2.7187113 × 108
Coefficient of variation (CV)2.9613402
Kurtosis151.78714
Mean91806786
Median Absolute Deviation (MAD)13235852
Skewness9.3299213
Sum1.902053 × 1012
Variance7.3913911 × 1016
MonotonicityNot monotonic
2025-04-18T07:31:08.584293image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 471
 
2.3%
25883254 10
 
< 0.1%
6639 10
 
< 0.1%
3020790 10
 
< 0.1%
99348431 10
 
< 0.1%
3877674 10
 
< 0.1%
312226510 10
 
< 0.1%
21912 10
 
< 0.1%
13099675 10
 
< 0.1%
1012205556 10
 
< 0.1%
Other values (19235) 20157
97.3%
ValueCountFrequency (%)
0 471
2.3%
2 1
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
15 1
 
< 0.1%
21 2
 
< 0.1%
24 1
 
< 0.1%
26 1
 
< 0.1%
28 1
 
< 0.1%
31 1
 
< 0.1%
ValueCountFrequency (%)
8079649362 1
< 0.1%
8079646911 1
< 0.1%
5908398479 1
< 0.1%
5773798407 1
< 0.1%
5773797147 1
< 0.1%
4898831101 1
< 0.1%
4821016218 1
< 0.1%
4679767471 1
< 0.1%
3817733132 1
< 0.1%
3725748519 1
< 0.1%

Likes
Real number (ℝ)

Zeros 

Distinct17939
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean646019.53
Minimum0
Maximum50788652
Zeros559
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size162.0 KiB
2025-04-18T07:31:08.758651image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile188.55
Q117584
median115136
Q3499660.75
95-th percentile2965245.5
Maximum50788652
Range50788652
Interquartile range (IQR)482076.75

Descriptive statistics

Standard deviation1768972.5
Coefficient of variation (CV)2.7382648
Kurtosis138.51205
Mean646019.53
Median Absolute Deviation (MAD)112831.5
Skewness8.7671533
Sum1.3384233 × 1010
Variance3.1292637 × 1012
MonotonicityNot monotonic
2025-04-18T07:31:08.932498image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 559
 
2.7%
5730 14
 
0.1%
12 13
 
0.1%
32 13
 
0.1%
1 12
 
0.1%
256 12
 
0.1%
21 11
 
0.1%
92753 10
 
< 0.1%
143480 10
 
< 0.1%
62411 10
 
< 0.1%
Other values (17929) 20054
96.8%
ValueCountFrequency (%)
0 559
2.7%
1 12
 
0.1%
2 6
 
< 0.1%
3 2
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
7 3
 
< 0.1%
8 2
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
50788652 1
< 0.1%
50788626 1
< 0.1%
40147674 1
< 0.1%
40147618 1
< 0.1%
35892575 1
< 0.1%
31047780 1
< 0.1%
27588224 1
< 0.1%
27588189 1
< 0.1%
26446178 1
< 0.1%
26399133 1
< 0.1%

Comments
Real number (ℝ)

Skewed  Zeros 

Distinct10485
Distinct (%)50.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26763.211
Minimum0
Maximum16083138
Zeros1068
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size162.0 KiB
2025-04-18T07:31:09.099621image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1408.25
median3008
Q313729
95-th percentile98965.8
Maximum16083138
Range16083138
Interquartile range (IQR)13320.75

Descriptive statistics

Standard deviation190615.65
Coefficient of variation (CV)7.1223013
Kurtosis2940.7487
Mean26763.211
Median Absolute Deviation (MAD)2969
Skewness44.284517
Sum5.544802 × 108
Variance3.6334326 × 1010
MonotonicityNot monotonic
2025-04-18T07:31:09.262230image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1068
 
5.2%
2 85
 
0.4%
1 85
 
0.4%
4 63
 
0.3%
7 58
 
0.3%
6 54
 
0.3%
3 51
 
0.2%
5 49
 
0.2%
21 46
 
0.2%
12 41
 
0.2%
Other values (10475) 19118
92.3%
ValueCountFrequency (%)
0 1068
5.2%
1 85
 
0.4%
2 85
 
0.4%
3 51
 
0.2%
4 63
 
0.3%
5 49
 
0.2%
6 54
 
0.3%
7 58
 
0.3%
8 37
 
0.2%
9 34
 
0.2%
ValueCountFrequency (%)
16083138 1
< 0.1%
9131761 1
< 0.1%
6535721 1
< 0.1%
6535719 1
< 0.1%
5331537 1
< 0.1%
5130725 1
< 0.1%
4805805 1
< 0.1%
4252791 2
< 0.1%
3637659 1
< 0.1%
3486944 1
< 0.1%
Distinct17393
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Memory size22.4 MiB
2025-04-18T07:31:09.731579image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length6786
Median length3290
Mean length1083.9426
Min length1

Characters and Unicode

Total characters22457122
Distinct characters97
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15750 ?
Unique (%)76.0%

Sample

1st rowOfficial HD Video for Gorillaz' fantastic track Feel Good Inc. Follow Gorillaz online: http://gorillaz.com http://facebook.com/Gorillaz http://twitter.com/GorillazBand http://instagram/Gorillaz For more information on Gorillaz don't forget to check out the official website at http://www.gorillaz.com
2nd rowThe official video for Gorillaz - Rhinestone Eyes Rhinestone Eyes is taken from the 2010 album Plastic Beach including the singles Rhinestone Eyes, Stylo, Superfast Jellyfish and On Melancholy Hill. Follow Gorillaz online: https://instagram.com/gorillaz https://tiktok.com/@gorillaz https://twitter.com/gorillaz https://facebook.com/gorillaz https://gorillaz.com #Gorillaz #RhinestoneEyes #PlasticBeach
3rd rowGorillaz - New Gold ft. Tame Impala & Bootie Brown (Official Visualiser) Pre-order Cracker Island album: https://gorillaz.com Listen to New Gold: https://gorill.az/newgold Join The Last Cult: https://thelastcult.org Listen to Gorillaz: https://gorill.az/listen Shop Gorillaz: https://store.gorillaz.com/ Tour gorillaz.com/tour Follow Gorillaz: https://instagram.com/gorillaz https://tiktok.com/@gorillaz https://twitter.com/gorillaz https://facebook.com/gorillaz https://gorillaz.com Director: Jamie Hewlett Co-Direction: Swear Studio, Steve Gallagher Exec Producers: Jamie Hewlett & Damon Albarn Producer: Alexa Pearson Video Design: Catherine Woodhouse Video Design: SHOP #newgold #crackerisland #gorillaz #newmusic #newalbum
4th rowFollow Gorillaz online: http://gorillaz.com http://facebook.com/Gorillaz http://twitter.com/GorillazBand https://instagram.com/gorillaz Music video by Gorillaz performing On Melancholy Hill. (P) 2010 The copyright in this audiovisual recording is owned by Parlophone Records
5th rowThe official music video for Gorillaz - Clint Eastwood Taken from Gorillaz debut Studio Album 'Gorillaz' released in 2001, which features the singles Clint Eastwood, 19-2000, Rock The House & Tomorrow Comes Today. Pre-order Cracker Island album: https://gorillaz.com Subscribe to the Gorillaz channel for all the best and latest official music videos, behind the scenes and live performances here - https://bit.ly/subscribegorillaz Listen to more from the album 'Gorillaz' here https://www.youtube.com/playlist?list=OLAK5uy_nCZqWcwZb_UQ0lmFgYUHiywAlyKVBP_uA See more official videos from Gorillaz here: https://www.youtube.com/playlist?list=PLtKoi37ubAW01MrQNue2ekVg5k9jrlLyo Follow Gorillaz: https://instagram.com/gorillaz https://tiktok.com/@gorillaz https://twitter.com/gorillaz https://facebook.com/gorillaz https://gorillaz.com LYRICS: I ain't happy, I'm feeling glad I got sunshine in a bag I'm useless but not for long The future is coming on I ain't happy, I'm feeling glad I got sunshine in a bag I'm useless but not for long The future is coming on It's coming on, it's coming on It's coming on, it's coming on Finally someone let me out of my cage Now time for me is nothin', 'cause I'm countin' no age Now I couldn't be there, now you shouldn't be scared I'm good at repairs, and I'm under each snare Intangible (ah y'all), bet you didn't think So I command you to, panoramic view (you) Look, I'll make it all manageable Pick and choose, sit and lose all you different crews Chicks and dudes, who you think is really kicking tunes? Picture you getting down in a picture tube Like you lit the fuse, you think it's fictional? Mystical? Maybe Spiritual hero who appears on you to clear your view When you're too crazy Lifeless to those the definition for what life is Priceless to you because I put you on the hype shift Did you like it? Gut smokin' righteous with one toke You're psychic among knows possess you with one go I ain't happy, I'm feeling glad I got sunshine in a bag I'm useless but not for long The future is coming on I ain't happy, I'm feeling glad I got sunshine in a bag I'm useless but not for long The future (that's right) is coming on It's coming on, it's coming on It's coming on, it's coming on The essence, the basics without did you make it? Allow me to make this childlike in nature Rhythm, you have it or you don't That's a fallacy, I'm in them Every sprouting tree, every child of peace Every cloud and sea, you see with your eyes I see destruction and demise Corruption in the skies (that's right) From this fucking enterprise, now I'm sucked into your lies Through Russel, not his muscles But percussion he provides For me as a guide, y'all can see me now 'Cause you don't see with your eye You perceive with your mind, that's the inner (fuck 'em) So I'mma stick around with Russ and be a mentor Bust a few rhymes so motherfuckers remember Where the thought is, I brought all this So you can survive when law is lawless (right here) Feeling sensations that you thought was dead No squealing and remember that it's all in your head I ain't happy, I'm feeling glad I got sunshine in a bag I'm useless but not for long The future is coming on I ain't happy, I'm feeling glad I got sunshine in a bag I'm useless but not for long My future is coming on It's coming on, it's coming on It's coming on, it's coming on My future It's coming on, it's coming on, it's coming on It's coming on, it's coming on, my future It's coming on, it's coming on, it's coming on It's coming on, it's coming on, my future It's coming on, it's coming on, it's coming on My future It's coming on, it's coming on, it's coming on My future It's coming on, it's coming on, it's coming on My future About Gorillaz: Virtual band Gorillaz is singer 2D, bassist Murdoc Niccals, guitarist Noodle and drummer Russel Hobbs. Created by Damon Albarn and Jamie Hewlett, their acclaimed eponymous debut album was released in 2001. The BRIT and Grammy Award winning band's subsequent albums are Demon Days (2005), Plastic Beach (2010), The Fall (2011), Humanz (2017) and The Now Now (2018). A truly global phenomenon, Gorillaz have achieved success in entirely ground-breaking ways, touring the world from San Diego to Syria, winning numerous awards including the coveted Jim Henson Creativity Honor. * The band are recognised by The Guinness Book Of World Records as the planet's Most Successful Virtual Act. #Gorillaz #ClintEastwood
ValueCountFrequency (%)
85225
 
2.7%
the 68560
 
2.1%
to 41399
 
1.3%
i 40188
 
1.3%
you 38593
 
1.2%
a 38550
 
1.2%
and 35623
 
1.1%
me 30947
 
1.0%
music 23690
 
0.7%
que 22927
 
0.7%
Other values (190244) 2786463
86.7%
2025-04-18T07:31:10.406661image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2825513
 
12.6%
e 1626332
 
7.2%
a 1365266
 
6.1%
o 1350465
 
6.0%
t 1253356
 
5.6%
i 1111319
 
4.9%
n 990601
 
4.4%
s 913768
 
4.1%
r 901630
 
4.0%
l 695024
 
3.1%
Other values (87) 9423848
42.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22457122
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2825513
 
12.6%
e 1626332
 
7.2%
a 1365266
 
6.1%
o 1350465
 
6.0%
t 1253356
 
5.6%
i 1111319
 
4.9%
n 990601
 
4.4%
s 913768
 
4.1%
r 901630
 
4.0%
l 695024
 
3.1%
Other values (87) 9423848
42.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22457122
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2825513
 
12.6%
e 1626332
 
7.2%
a 1365266
 
6.1%
o 1350465
 
6.0%
t 1253356
 
5.6%
i 1111319
 
4.9%
n 990601
 
4.4%
s 913768
 
4.1%
r 901630
 
4.0%
l 695024
 
3.1%
Other values (87) 9423848
42.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22457122
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2825513
 
12.6%
e 1626332
 
7.2%
a 1365266
 
6.1%
o 1350465
 
6.0%
t 1253356
 
5.6%
i 1111319
 
4.9%
n 990601
 
4.4%
s 913768
 
4.1%
r 901630
 
4.0%
l 695024
 
3.1%
Other values (87) 9423848
42.0%

Licensed
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
True
14140 
False
6108 
Non-Youtube
 
470

Length

Max length11
Median length4
Mean length4.4536152
Min length4

Characters and Unicode

Total characters92270
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowTrue
3rd rowTrue
4th rowTrue
5th rowTrue

Common Values

ValueCountFrequency (%)
True 14140
68.2%
False 6108
29.5%
Non-Youtube 470
 
2.3%

Length

2025-04-18T07:31:10.580439image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-18T07:31:10.702130image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
true 14140
68.2%
false 6108
29.5%
non-youtube 470
 
2.3%

Most occurring characters

ValueCountFrequency (%)
e 20718
22.5%
u 15080
16.3%
T 14140
15.3%
r 14140
15.3%
F 6108
 
6.6%
a 6108
 
6.6%
l 6108
 
6.6%
s 6108
 
6.6%
o 940
 
1.0%
N 470
 
0.5%
Other values (5) 2350
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 92270
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 20718
22.5%
u 15080
16.3%
T 14140
15.3%
r 14140
15.3%
F 6108
 
6.6%
a 6108
 
6.6%
l 6108
 
6.6%
s 6108
 
6.6%
o 940
 
1.0%
N 470
 
0.5%
Other values (5) 2350
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 92270
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 20718
22.5%
u 15080
16.3%
T 14140
15.3%
r 14140
15.3%
F 6108
 
6.6%
a 6108
 
6.6%
l 6108
 
6.6%
s 6108
 
6.6%
o 940
 
1.0%
N 470
 
0.5%
Other values (5) 2350
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 92270
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 20718
22.5%
u 15080
16.3%
T 14140
15.3%
r 14140
15.3%
F 6108
 
6.6%
a 6108
 
6.6%
l 6108
 
6.6%
s 6108
 
6.6%
o 940
 
1.0%
N 470
 
0.5%
Other values (5) 2350
 
2.5%

official_video
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
True
15723 
False
4525 
Non-Youtube
 
470

Length

Max length11
Median length4
Mean length4.3772082
Min length4

Characters and Unicode

Total characters90687
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowTrue
3rd rowTrue
4th rowTrue
5th rowTrue

Common Values

ValueCountFrequency (%)
True 15723
75.9%
False 4525
 
21.8%
Non-Youtube 470
 
2.3%

Length

2025-04-18T07:31:10.845058image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-18T07:31:10.981864image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
true 15723
75.9%
false 4525
 
21.8%
non-youtube 470
 
2.3%

Most occurring characters

ValueCountFrequency (%)
e 20718
22.8%
u 16663
18.4%
T 15723
17.3%
r 15723
17.3%
F 4525
 
5.0%
a 4525
 
5.0%
l 4525
 
5.0%
s 4525
 
5.0%
o 940
 
1.0%
N 470
 
0.5%
Other values (5) 2350
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90687
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 20718
22.8%
u 16663
18.4%
T 15723
17.3%
r 15723
17.3%
F 4525
 
5.0%
a 4525
 
5.0%
l 4525
 
5.0%
s 4525
 
5.0%
o 940
 
1.0%
N 470
 
0.5%
Other values (5) 2350
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90687
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 20718
22.8%
u 16663
18.4%
T 15723
17.3%
r 15723
17.3%
F 4525
 
5.0%
a 4525
 
5.0%
l 4525
 
5.0%
s 4525
 
5.0%
o 940
 
1.0%
N 470
 
0.5%
Other values (5) 2350
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90687
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 20718
22.8%
u 16663
18.4%
T 15723
17.3%
r 15723
17.3%
F 4525
 
5.0%
a 4525
 
5.0%
l 4525
 
5.0%
s 4525
 
5.0%
o 940
 
1.0%
N 470
 
0.5%
Other values (5) 2350
 
2.6%

Stream
Real number (ℝ)

Distinct18644
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.339019 × 108
Minimum0
Maximum3.3865203 × 109
Zeros11
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size162.0 KiB
2025-04-18T07:31:11.114814image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2328810.2
Q117393997
median48987283
Q31.3580659 × 108
95-th percentile5.6852717 × 108
Maximum3.3865203 × 109
Range3.3865203 × 109
Interquartile range (IQR)1.184126 × 108

Descriptive statistics

Standard deviation2.416096 × 108
Coefficient of variation (CV)1.8043777
Kurtosis23.66181
Mean1.339019 × 108
Median Absolute Deviation (MAD)39491201
Skewness4.153085
Sum2.7741796 × 1012
Variance5.83752 × 1016
MonotonicityNot monotonic
2025-04-18T07:31:11.400887image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169769959 24
 
0.1%
89466323 19
 
0.1%
0 11
 
0.1%
68848994 10
 
< 0.1%
48533462 10
 
< 0.1%
557368632 10
 
< 0.1%
21912 10
 
< 0.1%
9999321 9
 
< 0.1%
179230151 9
 
< 0.1%
23128326 9
 
< 0.1%
Other values (18634) 20597
99.4%
ValueCountFrequency (%)
0 11
0.1%
2 1
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
15 1
 
< 0.1%
21 2
 
< 0.1%
24 1
 
< 0.1%
28 1
 
< 0.1%
34 1
 
< 0.1%
38 1
 
< 0.1%
ValueCountFrequency (%)
3386520288 1
< 0.1%
3362005201 1
< 0.1%
2634013335 1
< 0.1%
2594926619 1
< 0.1%
2538329799 2
< 0.1%
2522431995 1
< 0.1%
2456205158 2
< 0.1%
2369272335 1
< 0.1%
2365777505 2
< 0.1%
2336219850 2
< 0.1%

youtube_spotify_ratio
Real number (ℝ)

Skewed  Zeros 

Distinct19779
Distinct (%)95.5%
Missing11
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2.055291
Minimum0
Maximum13511.076
Zeros460
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size162.0 KiB
2025-04-18T07:31:11.571080image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00084149906
Q10.06584966
median0.30307345
Q30.88629995
95-th percentile3.3420348
Maximum13511.076
Range13511.076
Interquartile range (IQR)0.82045029

Descriptive statistics

Standard deviation98.389965
Coefficient of variation (CV)47.871549
Kurtosis17277.181
Mean2.055291
Median Absolute Deviation (MAD)0.28287481
Skewness128.04011
Sum42558.911
Variance9680.5852
MonotonicityNot monotonic
2025-04-18T07:31:11.743538image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 460
 
2.2%
1 156
 
0.8%
0.004505102107 7
 
< 0.1%
0.004505107997 6
 
< 0.1%
0.34819031 5
 
< 0.1%
0.01318193816 4
 
< 0.1%
0.2761522568 4
 
< 0.1%
0.007167863143 4
 
< 0.1%
0.0004314537715 4
 
< 0.1%
0.03742969746 3
 
< 0.1%
Other values (19769) 20054
96.8%
(Missing) 11
 
0.1%
ValueCountFrequency (%)
0 460
2.2%
2.588155307 × 10-81
 
< 0.1%
5.171064485 × 10-71
 
< 0.1%
5.954901044 × 10-71
 
< 0.1%
1.009612708 × 10-61
 
< 0.1%
1.548390018 × 10-61
 
< 0.1%
1.559958476 × 10-62
 
< 0.1%
1.871510062 × 10-61
 
< 0.1%
2.221112283 × 10-61
 
< 0.1%
2.875824776 × 10-61
 
< 0.1%
ValueCountFrequency (%)
13511.07624 1
< 0.1%
3811.553684 1
< 0.1%
1617.425511 1
< 0.1%
323.0200073 1
< 0.1%
273.772115 1
< 0.1%
252.7103259 1
< 0.1%
239.7192478 1
< 0.1%
235.9588781 1
< 0.1%
215.6508484 1
< 0.1%
176.5704847 1
< 0.1%

success_category
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Spotify Dominant
16240 
YouTube Dominant
4478 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters331488
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSpotify Dominant
2nd rowSpotify Dominant
3rd rowSpotify Dominant
4th rowSpotify Dominant
5th rowYouTube Dominant

Common Values

ValueCountFrequency (%)
Spotify Dominant 16240
78.4%
YouTube Dominant 4478
 
21.6%

Length

2025-04-18T07:31:11.880486image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-18T07:31:12.001606image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
dominant 20718
50.0%
spotify 16240
39.2%
youtube 4478
 
10.8%

Most occurring characters

ValueCountFrequency (%)
o 41436
12.5%
n 41436
12.5%
t 36958
11.1%
i 36958
11.1%
D 20718
 
6.2%
m 20718
 
6.2%
a 20718
 
6.2%
20718
 
6.2%
S 16240
 
4.9%
p 16240
 
4.9%
Other values (7) 59348
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331488
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 41436
12.5%
n 41436
12.5%
t 36958
11.1%
i 36958
11.1%
D 20718
 
6.2%
m 20718
 
6.2%
a 20718
 
6.2%
20718
 
6.2%
S 16240
 
4.9%
p 16240
 
4.9%
Other values (7) 59348
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331488
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 41436
12.5%
n 41436
12.5%
t 36958
11.1%
i 36958
11.1%
D 20718
 
6.2%
m 20718
 
6.2%
a 20718
 
6.2%
20718
 
6.2%
S 16240
 
4.9%
p 16240
 
4.9%
Other values (7) 59348
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331488
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 41436
12.5%
n 41436
12.5%
t 36958
11.1%
i 36958
11.1%
D 20718
 
6.2%
m 20718
 
6.2%
a 20718
 
6.2%
20718
 
6.2%
S 16240
 
4.9%
p 16240
 
4.9%
Other values (7) 59348
17.9%

title_length
Real number (ℝ)

Distinct114
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.287479
Minimum3
Maximum188
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size162.0 KiB
2025-04-18T07:31:12.140877image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile16.85
Q136
median47
Q361
95-th percentile90
Maximum188
Range185
Interquartile range (IQR)25

Descriptive statistics

Standard deviation20.665582
Coefficient of variation (CV)0.41928664
Kurtosis0.3891041
Mean49.287479
Median Absolute Deviation (MAD)12
Skewness0.47789187
Sum1021138
Variance427.06626
MonotonicityNot monotonic
2025-04-18T07:31:12.314005image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 524
 
2.5%
46 493
 
2.4%
47 474
 
2.3%
49 472
 
2.3%
41 472
 
2.3%
44 471
 
2.3%
48 456
 
2.2%
43 447
 
2.2%
40 438
 
2.1%
50 438
 
2.1%
Other values (104) 16033
77.4%
ValueCountFrequency (%)
3 4
 
< 0.1%
4 13
 
0.1%
5 18
 
0.1%
6 24
 
0.1%
7 20
 
0.1%
8 30
 
0.1%
9 34
 
0.2%
10 39
 
0.2%
11 524
2.5%
12 49
 
0.2%
ValueCountFrequency (%)
188 4
< 0.1%
176 1
 
< 0.1%
142 1
 
< 0.1%
140 1
 
< 0.1%
119 2
< 0.1%
116 3
< 0.1%
115 3
< 0.1%
112 1
 
< 0.1%
111 2
< 0.1%
109 3
< 0.1%

title_word_count
Real number (ℝ)

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7366058
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size162.0 KiB
2025-04-18T07:31:12.464628image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q16
median8
Q311
95-th percentile16
Maximum30
Range29
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.7812058
Coefficient of variation (CV)0.43280031
Kurtosis0.75347654
Mean8.7366058
Median Absolute Deviation (MAD)2
Skewness0.59649266
Sum181005
Variance14.297517
MonotonicityNot monotonic
2025-04-18T07:31:12.602444image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
8 2580
12.5%
7 2518
12.2%
9 2394
11.6%
6 2114
10.2%
10 1882
9.1%
11 1511
7.3%
5 1463
7.1%
12 1060
 
5.1%
4 891
 
4.3%
13 798
 
3.9%
Other values (20) 3507
16.9%
ValueCountFrequency (%)
1 586
 
2.8%
2 191
 
0.9%
3 454
 
2.2%
4 891
 
4.3%
5 1463
7.1%
6 2114
10.2%
7 2518
12.2%
8 2580
12.5%
9 2394
11.6%
10 1882
9.1%
ValueCountFrequency (%)
30 1
 
< 0.1%
29 1
 
< 0.1%
28 5
 
< 0.1%
27 1
 
< 0.1%
26 3
 
< 0.1%
25 1
 
< 0.1%
24 2
 
< 0.1%
23 9
 
< 0.1%
22 24
0.1%
21 28
0.1%

title_length_cat
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.8 KiB
Very Short
4405 
Long
4153 
Short
4092 
Medium
4080 
Very Long
3988 

Length

Max length10
Median length6
Mean length6.8295202
Min length4

Characters and Unicode

Total characters141494
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowShort
2nd rowVery Long
3rd rowVery Long
4th rowMedium
5th rowShort

Common Values

ValueCountFrequency (%)
Very Short 4405
21.3%
Long 4153
20.0%
Short 4092
19.8%
Medium 4080
19.7%
Very Long 3988
19.2%

Length

2025-04-18T07:31:12.743968image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-18T07:31:12.882209image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
short 8497
29.2%
very 8393
28.8%
long 8141
28.0%
medium 4080
14.0%

Most occurring characters

ValueCountFrequency (%)
r 16890
11.9%
o 16638
11.8%
e 12473
 
8.8%
t 8497
 
6.0%
h 8497
 
6.0%
S 8497
 
6.0%
V 8393
 
5.9%
8393
 
5.9%
y 8393
 
5.9%
L 8141
 
5.8%
Other values (7) 36682
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 141494
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 16890
11.9%
o 16638
11.8%
e 12473
 
8.8%
t 8497
 
6.0%
h 8497
 
6.0%
S 8497
 
6.0%
V 8393
 
5.9%
8393
 
5.9%
y 8393
 
5.9%
L 8141
 
5.8%
Other values (7) 36682
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 141494
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 16890
11.9%
o 16638
11.8%
e 12473
 
8.8%
t 8497
 
6.0%
h 8497
 
6.0%
S 8497
 
6.0%
V 8393
 
5.9%
8393
 
5.9%
y 8393
 
5.9%
L 8141
 
5.8%
Other values (7) 36682
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 141494
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 16890
11.9%
o 16638
11.8%
e 12473
 
8.8%
t 8497
 
6.0%
h 8497
 
6.0%
S 8497
 
6.0%
V 8393
 
5.9%
8393
 
5.9%
y 8393
 
5.9%
L 8141
 
5.8%
Other values (7) 36682
25.9%

Interactions

2025-04-18T07:30:51.057587image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:05.390312image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:08.663459image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:11.858006image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:14.573653image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:17.272356image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:19.833864image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:22.272380image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:24.857094image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:27.271129image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:29.639990image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:32.407889image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:34.780762image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:37.005611image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:39.482041image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:41.673698image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:44.054664image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:46.394450image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:48.626206image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:51.152762image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:05.528412image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:08.800817image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:12.139867image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:14.707860image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:17.408905image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:19.957412image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:22.389806image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:24.979646image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:27.372600image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:29.817788image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:32.527912image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:34.876141image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:37.133450image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:39.577453image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:41.784075image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:44.170853image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:46.503550image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:48.716929image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:51.274314image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:05.653886image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:08.923845image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:12.279934image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:14.834952image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:17.529179image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:20.090101image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:22.511174image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:25.089604image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:27.488897image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:29.972927image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:32.664869image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:34.986471image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:37.251266image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:39.692351image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:41.895111image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:44.296208image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:46.601852image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:49.113427image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:51.400159image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:05.778902image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:09.058336image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:12.410958image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:14.962286image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:17.656968image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:20.222466image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:22.622893image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:25.229182image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:27.589272image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:30.119643image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:32.796898image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:35.103392image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:37.362082image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:39.786137image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:42.005597image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:44.411930image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:46.720424image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:49.219434image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:51.521253image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:05.964468image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:09.191174image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:12.541589image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:15.090727image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:17.773535image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:20.348887image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:22.747639image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:25.349336image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:27.689412image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:30.299221image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:32.907575image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:35.199275image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:37.472757image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:39.904293image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:42.137077image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:44.538023image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:46.839113image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:49.329626image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:51.616930image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:06.095141image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:09.341007image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:12.676757image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:15.227545image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:17.906909image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:20.473672image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:22.856272image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:25.473174image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:27.805944image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:30.473807image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:33.039070image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:35.333558image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:37.583211image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:39.999234image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:42.255891image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:44.648555image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:46.945528image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:49.448126image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:51.738416image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:06.247690image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:09.474005image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:12.807516image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:15.372711image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:18.023590image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:20.592261image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:22.995981image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:25.622232image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:27.939772image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:30.807884image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:33.162880image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:35.479052image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:37.695182image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:40.110514image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:42.387351image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:44.778789image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:47.059536image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:49.560690image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:51.849151image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:06.449822image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:09.636727image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:12.940957image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:15.501694image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:18.164795image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:20.720045image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:23.140065image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:25.739959image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:28.055971image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:30.922512image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:33.268435image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:35.588146image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:37.801288image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:40.221070image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:42.508073image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:44.904180image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:47.166799image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:49.662063image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:51.950435image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:06.830551image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:09.891585image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:13.057158image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:15.626911image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:18.290356image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:20.839645image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:23.406601image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:25.890080image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:28.187735image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:31.049781image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:33.379616image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:35.705156image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:37.912333image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:40.333089image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:42.626139image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:45.022042image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:47.285994image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:49.784386image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:52.067271image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:07.011733image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:10.118778image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:13.173219image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:15.773935image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:18.423508image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:20.966638image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:23.541705image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:26.023440image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:28.288756image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:31.151058image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:33.475060image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:35.823673image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:38.039066image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:40.437264image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:42.735243image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:45.136881image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:47.387974image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:49.895977image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:52.220251image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:07.262934image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:10.369600image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:13.307461image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:15.906989image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:18.557513image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:21.089786image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:23.661658image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:26.139968image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:28.406052image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:31.278163image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:33.613254image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:35.934146image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:38.150183image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:40.532342image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:42.845597image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:45.237182image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:47.512895image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:50.006065image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:52.336549image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:07.415738image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:10.527495image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:13.467500image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:16.041300image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:18.674990image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:21.225102image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:23.772825image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:26.273339image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:28.504904image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:31.405295image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:33.738047image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:36.046404image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:38.268145image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:40.642610image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:42.972413image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:45.368786image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:47.647197image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:50.117956image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:52.452297image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:07.556382image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:10.658698image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:13.591129image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:16.174286image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:18.807369image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:21.340168image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:23.890920image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:26.393410image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:28.622081image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:31.535066image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:33.833406image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:36.146451image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:38.368340image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:40.753112image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:43.095169image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:45.501608image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:47.769905image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:50.230541image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:52.577794image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:07.710901image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:10.843301image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:13.729148image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:16.329791image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:18.957164image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:21.473466image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:24.023593image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:26.527348image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:28.738947image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:31.681190image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:33.971514image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:36.269329image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:38.493388image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:40.868777image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:43.226446image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:45.641645image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:47.888654image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:50.348973image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:52.699779image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:07.875713image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:10.971914image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:13.857998image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:16.458389image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:19.073653image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:21.606428image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:24.163662image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:26.639508image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:28.839952image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:31.795279image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:34.107171image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:36.371471image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:38.630167image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:40.994977image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:43.354514image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:45.763843image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:47.999976image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:50.460253image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:52.865583image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:08.075183image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:11.124229image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:14.006312image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:16.607035image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:19.274078image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:21.740109image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:24.289935image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:26.773525image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:28.971012image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:31.921522image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:34.246389image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:36.510861image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:38.754981image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:41.116062image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:43.508850image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:45.901814image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:48.121503image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:50.587102image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:53.069162image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:08.228319image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:11.296201image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:14.152719image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:16.740411image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:19.423192image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:21.872854image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:24.422939image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:26.903270image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:29.105325image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:32.048622image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:34.375021image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:36.637362image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:38.883830image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:41.237745image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:43.635408image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:46.020385image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:48.264505image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:50.698813image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:53.370442image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:08.377791image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:11.457682image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:14.305833image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:16.890639image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:19.566233image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:22.012842image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:24.556326image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:27.023117image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:29.239295image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:32.154288image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:34.505977image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:36.751515image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:39.234273image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:41.368638image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:43.788861image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:46.152496image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:48.375443image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:50.818961image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:53.503825image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:08.511590image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:11.673999image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:14.440379image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:17.149933image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:19.691427image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:22.139548image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:24.690081image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:27.156044image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:29.421001image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:32.281350image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:34.654640image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:36.870575image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:39.354829image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:41.540243image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:43.920474image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:46.274812image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:48.502090image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-18T07:30:50.946278image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Missing values

2025-04-18T07:30:53.887069image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-18T07:30:54.448657image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-18T07:30:54.993795image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IndexArtistUrl_spotifyTrackAlbumAlbum_typeUriDanceabilityEnergyKeyLoudnessSpeechinessAcousticnessInstrumentalnessLivenessValenceTempoDuration_msUrl_youtubeTitleChannelViewsLikesCommentsDescriptionLicensedofficial_videoStreamyoutube_spotify_ratiosuccess_categorytitle_lengthtitle_word_counttitle_length_cat
00Gorillazhttps://open.spotify.com/artist/3AA28KZvwAUcZuOKwyblJQFeel Good Inc.Demon Daysalbumspotify:track:0d28khcov6AiegSCpG5TuT0.8180.7056.0-6.6790.17700.00840.00230.61300.772138.559222640.0https://www.youtube.com/watch?v=HyHNuVaZJ-kGorillaz - Feel Good Inc. (Official Video)Gorillaz693555221.06220896.0169907.0Official HD Video for Gorillaz' fantastic track Feel Good Inc.\n\nFollow Gorillaz online:\nhttp://gorillaz.com\nhttp://facebook.com/Gorillaz\nhttp://twitter.com/GorillazBand\nhttp://instagram/Gorillaz\n\nFor more information on Gorillaz don't forget to check out the official website at http://www.gorillaz.comTrueTrue1.040235e+090.666729Spotify Dominant427Short
11Gorillazhttps://open.spotify.com/artist/3AA28KZvwAUcZuOKwyblJQRhinestone EyesPlastic Beachalbumspotify:track:1foMv2HQwfQ2vntFf9HFeG0.6760.7038.0-5.8150.03020.08690.00070.04630.85292.761200173.0https://www.youtube.com/watch?v=yYDmaexVHicGorillaz - Rhinestone Eyes [Storyboard Film] (Official Music Video)Gorillaz72011645.01079128.031003.0The official video for Gorillaz - Rhinestone Eyes\n\nRhinestone Eyes is taken from the 2010 album Plastic Beach including the singles Rhinestone Eyes, Stylo, Superfast Jellyfish and On Melancholy Hill.\n\nFollow Gorillaz online:\nhttps://instagram.com/gorillaz \nhttps://tiktok.com/@gorillaz \nhttps://twitter.com/gorillaz \nhttps://facebook.com/gorillaz \nhttps://gorillaz.com\n\n#Gorillaz #RhinestoneEyes #PlasticBeachTrueTrue3.100837e+080.232233Spotify Dominant679Very Long
22Gorillazhttps://open.spotify.com/artist/3AA28KZvwAUcZuOKwyblJQNew Gold (feat. Tame Impala and Bootie Brown)New Gold (feat. Tame Impala and Bootie Brown)singlespotify:track:64dLd6rVqDLtkXFYrEUHIU0.6950.9231.0-3.9300.05220.04250.04690.11600.551108.014215150.0https://www.youtube.com/watch?v=qJa-VFwPpYAGorillaz - New Gold ft. Tame Impala & Bootie Brown (Official Visualiser)Gorillaz8435055.0282142.07399.0Gorillaz - New Gold ft. Tame Impala & Bootie Brown (Official Visualiser)\n\nPre-order Cracker Island album: https://gorillaz.com \nListen to New Gold: https://gorill.az/newgold\nJoin The Last Cult: https://thelastcult.org\nListen to Gorillaz: https://gorill.az/listen\nShop Gorillaz: https://store.gorillaz.com/\nTour gorillaz.com/tour\n\nFollow Gorillaz: \nhttps://instagram.com/gorillaz \nhttps://tiktok.com/@gorillaz\nhttps://twitter.com/gorillaz \nhttps://facebook.com/gorillaz \nhttps://gorillaz.com\n\nDirector: Jamie Hewlett\nCo-Direction: Swear Studio, Steve Gallagher\nExec Producers: Jamie Hewlett & Damon Albarn\nProducer: Alexa Pearson\nVideo Design: Catherine Woodhouse\nVideo Design: SHOP\n\n#newgold #crackerisland #gorillaz #newmusic #newalbumTrueTrue6.306347e+070.133755Spotify Dominant7212Very Long
33Gorillazhttps://open.spotify.com/artist/3AA28KZvwAUcZuOKwyblJQOn Melancholy HillPlastic Beachalbumspotify:track:0q6LuUqGLUiCPP1cbdwFs30.6890.7392.0-5.8100.02600.00000.50900.06400.578120.423233867.0https://www.youtube.com/watch?v=04mfKJWDSzIGorillaz - On Melancholy Hill (Official Video)Gorillaz211754952.01788577.055229.0Follow Gorillaz online:\nhttp://gorillaz.com \nhttp://facebook.com/Gorillaz\nhttp://twitter.com/GorillazBand\nhttps://instagram.com/gorillaz\n\nMusic video by Gorillaz performing On Melancholy Hill. (P) 2010 The copyright in this audiovisual recording is owned by Parlophone RecordsTrueTrue4.346636e+080.487170Spotify Dominant467Medium
44Gorillazhttps://open.spotify.com/artist/3AA28KZvwAUcZuOKwyblJQClint EastwoodGorillazalbumspotify:track:7yMiX7n9SBvadzox8T5jzT0.6630.69410.0-8.6270.17100.02530.00000.06980.525167.953340920.0https://www.youtube.com/watch?v=1V_xRb0x9awGorillaz - Clint Eastwood (Official Video)Gorillaz618480958.06197318.0155930.0The official music video for Gorillaz - Clint Eastwood\n\nTaken from Gorillaz debut Studio Album 'Gorillaz' released in 2001, which features the singles Clint Eastwood, 19-2000, Rock The House & Tomorrow Comes Today.\n\nPre-order Cracker Island album: https://gorillaz.com \n\nSubscribe to the Gorillaz channel for all the best and latest official music videos, behind the scenes and live performances here - https://bit.ly/subscribegorillaz\n\nListen to more from the album 'Gorillaz' here\nhttps://www.youtube.com/playlist?list=OLAK5uy_nCZqWcwZb_UQ0lmFgYUHiywAlyKVBP_uA\n\nSee more official videos from Gorillaz here: \nhttps://www.youtube.com/playlist?list=PLtKoi37ubAW01MrQNue2ekVg5k9jrlLyo\n\nFollow Gorillaz:\nhttps://instagram.com/gorillaz \nhttps://tiktok.com/@gorillaz \nhttps://twitter.com/gorillaz \nhttps://facebook.com/gorillaz \nhttps://gorillaz.com\n\nLYRICS:\nI ain't happy, I'm feeling glad\nI got sunshine in a bag\nI'm useless but not for long\nThe future is coming on\nI ain't happy, I'm feeling glad\nI got sunshine in a bag\nI'm useless but not for long\nThe future is coming on\nIt's coming on, it's coming on\nIt's coming on, it's coming on\n\nFinally someone let me out of my cage\nNow time for me is nothin', 'cause I'm countin' no age\nNow I couldn't be there, now you shouldn't be scared\nI'm good at repairs, and I'm under each snare\n\nIntangible (ah y'all), bet you didn't think\nSo I command you to, panoramic view (you)\nLook, I'll make it all manageable\nPick and choose, sit and lose all you different crews\nChicks and dudes, who you think is really kicking tunes?\n\nPicture you getting down in a picture tube\nLike you lit the fuse, you think it's fictional? Mystical? Maybe\nSpiritual hero who appears on you to clear your view\nWhen you're too crazy\n\nLifeless to those the definition for what life is\nPriceless to you because I put you on the hype shift\nDid you like it? Gut smokin' righteous with one toke\nYou're psychic among knows possess you with one go\n\nI ain't happy, I'm feeling glad\nI got sunshine in a bag\nI'm useless but not for long\nThe future is coming on\n\nI ain't happy, I'm feeling glad\nI got sunshine in a bag\nI'm useless but not for long\nThe future (that's right) is coming on\nIt's coming on, it's coming on\nIt's coming on, it's coming on\n\nThe essence, the basics without did you make it?\nAllow me to make this childlike in nature\nRhythm, you have it or you don't\nThat's a fallacy, I'm in them\nEvery sprouting tree, every child of peace\nEvery cloud and sea, you see with your eyes\n\nI see destruction and demise\nCorruption in the skies (that's right)\nFrom this fucking enterprise, now I'm sucked into your lies\nThrough Russel, not his muscles\nBut percussion he provides\n\nFor me as a guide, y'all can see me now\n'Cause you don't see with your eye\nYou perceive with your mind, that's the inner (fuck 'em)\nSo I'mma stick around with Russ and be a mentor\n\nBust a few rhymes so motherfuckers remember\nWhere the thought is, I brought all this\nSo you can survive when law is lawless (right here)\nFeeling sensations that you thought was dead\nNo squealing and remember that it's all in your head\n\nI ain't happy, I'm feeling glad\nI got sunshine in a bag\nI'm useless but not for long\nThe future is coming on\n\nI ain't happy, I'm feeling glad\nI got sunshine in a bag\nI'm useless but not for long\nMy future is coming on\nIt's coming on, it's coming on\nIt's coming on, it's coming on\n\nMy future\nIt's coming on, it's coming on, it's coming on\nIt's coming on, it's coming on, my future\nIt's coming on, it's coming on, it's coming on\nIt's coming on, it's coming on, my future\nIt's coming on, it's coming on, it's coming on\n\nMy future\nIt's coming on, it's coming on, it's coming on\nMy future\nIt's coming on, it's coming on, it's coming on\nMy future\n\nAbout Gorillaz:\nVirtual band Gorillaz is singer 2D, bassist Murdoc Niccals, guitarist Noodle and drummer Russel Hobbs. Created by Damon Albarn and Jamie Hewlett, their acclaimed eponymous debut album was released in 2001. The BRIT and Grammy Award winning band's subsequent albums are Demon Days (2005), Plastic Beach (2010), The Fall (2011), Humanz (2017) and The Now Now (2018). A truly global phenomenon, Gorillaz have achieved success in entirely ground-breaking ways, touring the world from San Diego to Syria, winning numerous awards including the coveted Jim Henson Creativity Honor.\n\n* The band are recognised by The Guinness Book Of World Records as the planet's Most Successful Virtual Act.\n\n#Gorillaz #ClintEastwoodTrueTrue6.172597e+081.001978YouTube Dominant426Short
55Gorillazhttps://open.spotify.com/artist/3AA28KZvwAUcZuOKwyblJQDAREDemon Daysalbumspotify:track:4Hff1IjRbLGeLgFgxvHflk0.7600.89111.0-5.8520.03720.02290.08690.29800.966120.264245000.0https://www.youtube.com/watch?v=uAOR6ib95kQGorillaz - DARE (Official Video)Gorillaz259021161.01844658.072008.0Follow Gorillaz online:\nhttp://gorillaz.com \nhttp://facebook.com/Gorillaz\nhttp://twitter.com/GorillazBand\nhttps://instagram.com/gorillaz\n\nStream Gorillaz on Spotify: http://bit.ly/1kvIyPR\n\nMusic video by Gorillaz performing DARE. (P) 2005 The copyright in this sound recording is owned by Parlophone RecordsTrueTrue3.238503e+080.799818Spotify Dominant325Very Short
66Gorillazhttps://open.spotify.com/artist/3AA28KZvwAUcZuOKwyblJQNew Gold (feat. Tame Impala and Bootie Brown) - Dom Dolla RemixNew Gold (feat. Tame Impala and Bootie Brown) [Dom Dolla Remix]singlespotify:track:2c3KCGq6UojB2c8UAFrRON0.7160.8974.0-7.1850.06290.01200.26200.32500.358127.030274142.0https://www.youtube.com/watch?v=BONNm0F7TtoGorillaz - New Gold ft. Tame Impala, Bootie Brown (Dom Dolla Remix) (Official Live Video)Dom Dolla451996.011686.0241.0Gorillaz 'New Gold' ft. Tame Impala, Bootie Brown (Dom Dolla Remix) \n\nLive @ Shrine Expo Hall, Los Angeles, Oct 7th 2022\n\nStream/Download New Gold (Dom Dolla Remix): https://DomDolla.lnk.to/NewGoldRemix\n\nFollow and listen to more Dom: https://DomDolla.lnk.to/FollowYo\nFacebook: https://www.facebook.com/domdollamusic/\nTwitter: https://twitter.com/domdolla\nInstagram: https://www.instagram.com/domdolla/\nSoundcloud: https://soundcloud.com/domdolla\nBeatport: https://www.beatport.com/artist/dom-d...\n\nCamera Operators:\nTrevor Thompson\nJonathan Marroquin\nGabe Tiano\nJustin Erougian\n\nEditor:\nShevin Dissanayake\n\nLighting / Production:\nSteve Lieberman - Lighting Design / Operation\nNathan Aveling - Laser Design / Operation\nDaniel Aldaz - VJ\n\n#DomDolla #Gorillaz #TameImpala #BootieBrown #RemixFalseTrue1.066615e+070.042377Spotify Dominant8915Very Long
77Gorillazhttps://open.spotify.com/artist/3AA28KZvwAUcZuOKwyblJQShe's My Collar (feat. Kali Uchis)Humanz (Deluxe)albumspotify:track:3lIxtCaROdRDuTnNBDm3n20.7260.81511.0-5.8860.03130.00800.08100.11200.462140.158209560.0https://www.youtube.com/watch?v=f8NwLXYIHS4Gorillaz - She's My Collar [HQ]SalvaMunox1010982.017675.0260.0BONUS: Humanz [DEFINITIVE EDITION]\nhttps://drive.google.com/file/d/1MsTWitzhoLzEG01q7dANDTZ_ltu-6Y5w/FalseFalse1.596059e+080.006334Spotify Dominant316Very Short
88Gorillazhttps://open.spotify.com/artist/3AA28KZvwAUcZuOKwyblJQCracker Island (feat. Thundercat)Cracker Island (feat. Thundercat)singlespotify:track:2W3ZpQg9i6lE6kmHbcdu9N0.7410.9132.0-3.3400.04650.00340.10300.32500.643120.012213750.0https://www.youtube.com/watch?v=S03T47hapAcGorillaz - Cracker Island ft. Thundercat (Official Video)Gorillaz24459820.0739527.020296.0Listen to Cracker Island: https://gorillaz.lnk.to/CrackerIsland \nJoin The Last Cult: https://thelastcult.org\nListen to Gorillaz: https://gorill.az/listen\nShop Gorillaz: https://store.gorillaz.com/\n\nFollow Gorillaz: \nhttps://instagram.com/gorillaz \nhttps://tiktok.com/@gorillaz\nhttps://twitter.com/gorillaz \nhttps://facebook.com/gorillaz \nhttps://gorillaz.com\n\n#crackerisland #gorillaz #newmusic #newmusicvideo\n\nCracker Island\n\nOn cracker Island it was born\nTo the collective of the dawn\nThey were planting seeds at night\n\nTo grow a made up paradise\nWhere the truth was auto tuned (forever cult)\nBut it's sadness I consumed (forever cult)\nInto my formats everyday (forever cult) \nin end I had to pay (what world is this)\nIn the end I had to pay (I purged my soul)\nIn the end I had to pay (I drank to riot) \nNothing more to say (I drank to riot)\n \nThey taught themselves to be occult\nThey didn't know it's many strategies x 2 \nFantasies \n\nWhat world is this? x2\n\nOn cracker island it was raised\nBy the collective from the grave\nIt only came out at night \nIt ate up their paradise (paradise)\nWhere the truth was auto tuned (forever cult)\nAnd it's sadness I consumed (forever cult)\nInto my formats everyday (forever cult)\nIn the end I had to pay (what world is this)\nOut there on my silverlake (I was not there)\nIn the end it will be great (beneath the hills)\nI'm like a ship between the tides (I saw myself)\nI held on I survived (there in the void)\n\nThey taught themselves to be occult\nThey didn't know it's many strategies x2\nFantasy\n\nOn cracker island It will die (forever cult)\nJoin the collective in the sky (forever cult)\nAnd on a shining boat of light (forever cult)\n\nGo up to paradise (what world is this) \nWhere the truth is autotuned (I purged my soul)\nAnd it's sadness I consume (I drank to riot)\nInto my formats everyday (I drank to riot)\nIn the end I had to pay (forever cult)\n\nIn the end I'll have to pay (forever cult)\nIn the end I'll be ok (forever cult)\n\nNothing more to say x3\n\nCredits:\nDirectors: Jamie Hewlett & Fx Goby \nExecutive Producers: Jamie Hewlett & Damon Albarn\nJamie Hewlett and Damon Albarn are managed by Eleven Management \nA Nexus Studios & Gorillaz Production \n\nExecutive Producers for Nexus: Charlotte Bavasso & Chris O'Reilly, Mike Bell \nCG Supervisors: Dave Hunt & Florian Caspar\nVFX Supervisor & Compositing Lead: German Diez \nDP: Ricky Patel\nEditor: Dave Slade \nProducer: Jo Bierton \nProduction Manager: Ruyi Meer \nProduction Coordinator: Tyler Antin \nProduction Assistant: Megan Silvester \nStoryboard Artist: Rodrigo Aquino Verdun\nCharacter Modeller: Andrew Hickinbottom\n3D Generalist: Andy Spence \nRigging: Niko Rossi, Nayla Nassar\nModeller & Texture Artist: Catarina Loja\nPipeline TD: Tom Melson \nAnimators: Fabrice Fiteni, Marylou Mao, Tom Lowe, Fatih Dogan\nLead Lighter: Carl Kenyon \nLighting & Rendering: Pawel Adamiec, Rodi Kaya, Daniel Prince\nLead Compositor; Sacha Danjou \nCompositors: Gareth Tredrea, Sander Saks, Hiral Kawa, Alexey Viatvud, Gourav Kumar, Simran Arora, Osman Baloglu\nVFX Editors: Andrea Zantiras, Zaki Fulford\nDesigner & Matte Painters: Milan Baulard, Callum Strachan, Paul Campion\nDesigner: Ieuan Lewis \n2D VFX ANIMATOR: Bethany Levy\n3D VFX: Paul Mitcheson\nMotion Graphics Designer: Nathan Bayliss\nHead of Resources: Natalie Busuttil \nResource Manager: Gigi Kohut \nResource Coordinator: Meg Dupont\nMarketing & PR: Valentina Tarelli, Nancy Edmondson, Isobel Wise, Steph Anjo\n\nArchive Producer: Lisa Savage \n\nSound FX Designer: JM Finch \n\nGrade Company: Black Kite Studios\n\nService Production Company - Tuna Icon\n\nHollywood(r) is the trademark and intellectual property of Hollywood Chamber of Commerce. All Rights Reserved.\n \nCAST:\n2D, Noodle, Russel Hobbs, Murdoc Niccals, Thundercat\n\nSupporting cast:\nMilica Tepavac, Sonja Vukicevic, Ivan Tomic, Rudolf Tsabalala, Vukasin Stanic, Ella Landesman, Sippa Hengprathom, Nani Pavlovic, Tea Simic, Snezana Radivojsa, Rajesh Kalhan, Roda Adan\n\nSpanish Service Co: AzuL \n\nEleven Management are:\nNiamh Byrne, Regine Moylett, Tanyel Vahdettin, Stars Redmond, Astrid Ferguson, Gabriele Power, Ellie Nolan, Aston New, Katherine Nash, Suzi Grossman, Selena Dion, Clare Moss, Hannah Norris\n\nWith thanks to:\nThundercat, Dom Smith, Alison Smith, Josh BermanTrueTrue4.267190e+070.573207Spotify Dominant578Long
99Gorillazhttps://open.spotify.com/artist/3AA28KZvwAUcZuOKwyblJQDirty HarryDemon Daysalbumspotify:track:2bfGNzdiRa1jXZRdfssSzR0.6250.87710.0-7.1760.16200.03150.08110.67200.865192.296230426.0https://www.youtube.com/watch?v=cLnkQAeMbIMGorillaz - Dirty Harry (Official Video)Gorillaz154761056.01386920.039240.0Follow Gorillaz online:\nhttp://gorillaz.com \nhttp://facebook.com/Gorillaz\nhttp://twitter.com/GorillazBand\nhttps://instagram.com/gorillaz\n\nStream Gorillaz on Spotify: http://bit.ly/1kvIyPR \n\nMusic video by Gorillaz performing Dirty Harry. (P) 2005 The copyright in this audiovisual recording is owned by Parlophone RecordsTrueTrue1.910747e+080.809950Spotify Dominant396Short
IndexArtistUrl_spotifyTrackAlbumAlbum_typeUriDanceabilityEnergyKeyLoudnessSpeechinessAcousticnessInstrumentalnessLivenessValenceTempoDuration_msUrl_youtubeTitleChannelViewsLikesCommentsDescriptionLicensedofficial_videoStreamyoutube_spotify_ratiosuccess_categorytitle_lengthtitle_word_counttitle_length_cat
2070820708SICK LEGENDhttps://open.spotify.com/artist/3EYY5FwDkHEYLw5V86SAtlPART OF ME HARDSTYLE (SPED UP)PART OF ME HARDSTYLE (SPED UP)singlespotify:track:6jkEAxBnX2PVchKYH9Y46D0.6420.94910.0-3.0550.03730.01940.22700.41400.6920101.959112971.0https://www.youtube.com/watch?v=XW762gf4tgkPART OF ME HARDSTYLE (SPED UP)SICK LEGEND - Topic40814.0640.00.0Provided to YouTube by Routenote\n\nPART OF ME HARDSTYLE (SPED UP) * SICK LEGEND\n\nPART OF ME HARDSTYLE (SPED UP)\n\n(p) SICK CVNT\n\nReleased on: 2022-07-08\n\nAuto-generated by YouTube.TrueTrue17721588.00.002303Spotify Dominant306Very Short
2070920709SICK LEGENDhttps://open.spotify.com/artist/3EYY5FwDkHEYLw5V86SAtlSUMMER TIME SADNESS HARDSTYLESUMMER TIME SADNESS HARDSTYLEsinglespotify:track:3P48rdupp9trbMA2J2Vsta0.4900.8241.0-6.3260.15800.08880.00000.36300.3200166.837104910.0https://www.youtube.com/watch?v=AHaIdOXzzuESUMMER TIME SADNESS HARDSTYLESICK LEGEND - Topic23719.0362.00.0Provided to YouTube by Routenote\n\nSUMMER TIME SADNESS HARDSTYLE * SICK LEGEND\n\nSUMMER TIME SADNESS HARDSTYLE\n\n(p) SICK CVNT\n\nReleased on: 2022-07-21\n\nAuto-generated by YouTube.TrueTrue10838254.00.002188Spotify Dominant294Very Short
2071020710SICK LEGENDhttps://open.spotify.com/artist/3EYY5FwDkHEYLw5V86SAtlPART OF ME HARDSTYLEPART OF ME HARDSTYLEsinglespotify:track:19gnl7xN5xAEwDquLNKl760.5190.9027.0-3.4940.04200.01200.00010.26600.6570174.790131657.0https://www.youtube.com/watch?v=TWqt-qOty2gPART OF ME HARDSTYLESICK LEGEND - Topic370711.04639.00.0Provided to YouTube by Routenote\n\nPART OF ME HARDSTYLE * SICK LEGEND\n\nPART OF ME HARDSTYLE\n\n(p) SICK CVNT\n\nReleased on: 2022-07-08\n\nAuto-generated by YouTube.TrueTrue16332133.00.022698Spotify Dominant204Very Short
2071120711SICK LEGENDhttps://open.spotify.com/artist/3EYY5FwDkHEYLw5V86SAtlMIDDLE OF THE NIGHT - HARDSTYLE REMIXMIDDLE OF THE NIGHT - HARDSTYLE REMIXsinglespotify:track:4pqAkUZlA17gsTxFjP4BDL0.2920.6922.0-7.1980.03760.00010.00040.38200.0544185.467175147.0https://www.youtube.com/watch?v=5f_RpP10nRkMIDDLE OF THE NIGHT - HARDSTYLE REMIXSICK LEGEND - Topic254268.03472.00.0Provided to YouTube by Routenote\n\nMIDDLE OF THE NIGHT - HARDSTYLE REMIX * SICK LEGEND\n\nMIDDLE OF THE NIGHT - HARDSTYLE REMIX\n\n(p) SICK LEGEND\n\nReleased on: 2022-08-01\n\nAuto-generated by YouTube.TrueTrue17125177.00.014848Spotify Dominant377Short
2071220712SICK LEGENDhttps://open.spotify.com/artist/3EYY5FwDkHEYLw5V86SAtlEVERYTIME WE TOUCH HARDSTYLE (SPED UP)EVERYTIME WE TOUCH HARDSTYLE (SPED UP)singlespotify:track:2dSNs47vHBSPnsUwpl39nk0.5540.8741.0-5.1990.04800.23500.00000.31800.6170102.16794000.0https://www.youtube.com/watch?v=2C66T9FhnAkEVERYTIME WE TOUCH HARDSTYLE (SPED UP)SICK LEGEND - Topic16004.0267.00.0Provided to YouTube by Routenote\n\nEVERYTIME WE TOUCH HARDSTYLE (SPED UP) * SICK LEGEND\n\nEVERYTIME WE TOUCH HARDSTYLE (SPED UP)\n\n(p) SICK CVNT\n\nReleased on: 2022-07-12\n\nAuto-generated by YouTube.TrueTrue9921887.00.001613Spotify Dominant386Short
2071320713SICK LEGENDhttps://open.spotify.com/artist/3EYY5FwDkHEYLw5V86SAtlJUST DANCE HARDSTYLEJUST DANCE HARDSTYLEsinglespotify:track:0RtcKQGyI4hr8FgFH1TuYG0.5820.9265.0-6.3440.03280.44800.00000.08390.658090.00294667.0https://www.youtube.com/watch?v=5SHmKFKlNqIJUST DANCE HARDSTYLESICK LEGEND - Topic71678.01113.00.0Provided to YouTube by Routenote\n\nJUST DANCE HARDSTYLE * SICK LEGEND\n\nJUST DANCE HARDSTYLE\n\n(p) SICK CVNT\n\nReleased on: 2022-07-12\n\nAuto-generated by YouTube.TrueTrue9227144.00.007768Spotify Dominant203Very Short
2071420714SICK LEGENDhttps://open.spotify.com/artist/3EYY5FwDkHEYLw5V86SAtlSET FIRE TO THE RAIN HARDSTYLESET FIRE TO THE RAIN HARDSTYLEsinglespotify:track:3rHvPA8lUnPBkaLyPOc0VV0.5310.9364.0-1.7860.13700.02800.00000.09230.6570174.869150857.0https://www.youtube.com/watch?v=ocTH6KxllDQSET FIRE TO THE RAIN HARDSTYLESICK LEGEND - Topic164741.02019.00.0Provided to YouTube by Routenote\n\nSET FIRE TO THE RAIN HARDSTYLE * SICK LEGEND\n\nSET FIRE TO THE RAIN HARDSTYLE\n\n(p) SICK CVNT\n\nReleased on: 2022-07-11\n\nAuto-generated by YouTube.TrueTrue10898176.00.015116Spotify Dominant306Very Short
2071520715SICK LEGENDhttps://open.spotify.com/artist/3EYY5FwDkHEYLw5V86SAtlOUTSIDE HARDSTYLE SPED UPOUTSIDE HARDSTYLE SPED UPsinglespotify:track:4jk00YxPtPbhvHJE9N4ddv0.4430.8304.0-4.6790.06470.02430.00000.15400.4190168.388136842.0https://www.youtube.com/watch?v=5wFhE-HY0hgOUTSIDE HARDSTYLE SPED UPSICK LEGEND - Topic35646.0329.00.0Provided to YouTube by Routenote\n\nOUTSIDE HARDSTYLE SPED UP * SICK LEGEND\n\nOUTSIDE HARDSTYLE SPED UP\n\n(p) SICK LEGEND\n\nReleased on: 2022-07-27\n\nAuto-generated by YouTube.TrueTrue6226110.00.005725Spotify Dominant254Very Short
2071620716SICK LEGENDhttps://open.spotify.com/artist/3EYY5FwDkHEYLw5V86SAtlONLY GIRL HARDSTYLEONLY GIRL HARDSTYLEsinglespotify:track:5EyErbpsugWliX006eTDex0.4170.7679.0-4.0040.41900.35600.01840.10800.5390155.378108387.0https://www.youtube.com/watch?v=VMFLbFRNCn0ONLY GIRL HARDSTYLESICK LEGEND - Topic6533.088.00.0Provided to YouTube by Routenote\n\nONLY GIRL HARDSTYLE * SICK LEGEND\n\nONLY GIRL HARDSTYLE\n\n(p) SICK LEGEND\n\nReleased on: 2022-08-01\n\nAuto-generated by YouTube.TrueTrue6873961.00.000950Spotify Dominant193Very Short
2071720717SICK LEGENDhttps://open.spotify.com/artist/3EYY5FwDkHEYLw5V86SAtlMISS YOU HARDSTYLEMISS YOU HARDSTYLEsinglespotify:track:6lOn0jz1QpjcWeXo1oMm0k0.4980.9386.0-4.5430.10700.00280.91100.13600.0787160.067181500.0https://www.youtube.com/watch?v=zau0dckCFi0MISS YOU HARDSTYLESICK LEGEND - Topic158697.02484.00.0Provided to YouTube by Routenote\n\nMISS YOU HARDSTYLE * SICK LEGEND\n\nMISS YOU HARDSTYLE\n\n(p) SICK CVNT\n\nReleased on: 2022-10-04\n\nAuto-generated by YouTube.TrueTrue5695584.00.027863Spotify Dominant183Very Short